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Large models, encompassing large language and diffusion models, have shown exceptional promise in approximating human-level intelligence, garnering significant interest from both academic and industrial spheres.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · 1901
Earlier work this paper cites.
Eda: Easy data augmentation techniques for boosting performance on text classification tasks
Wei, J. and Zou, K. (2019) · 1901
Earlier work this paper cites.
Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
McCoy, R. T., Pavlick, E., and Linzen, T. (2019) · 1902
Earlier work this paper cites.
Benchmarking natural language understanding services for building conversational agents
Liu, X., Eshghi, A., Swietojanski, P., and Rieser, V. (2019a) · 1903
Earlier work this paper cites.
Synthetic qa corpora generation with roundtrip consistency
Alberti, C., Andor, D., Pitler, E., Devlin, J., and Collins, M. (2019) · 1906
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019b) · 1907
Earlier work this paper cites.
Few-shot text classification with distributional signatures
Bao, Y., Wu, M., Chang, S., and Barzilay, R. (2019) · 1908
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N. and Gurevych, I. (2019) · 1908
Earlier work this paper cites.
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L. (2019) · 1910
Earlier work this paper cites.
Adversarial nli: A new benchmark for natural language understanding
Nie, Y., Williams, A., Dinan, E., Bansal, M., Weston, J., and Kiela, D. (2019) · 1910
Earlier work this paper cites.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., and Wolf, T. (2019) · 1910
Earlier work this paper cites.
Samsum corpus: A human-annotated dialogue dataset for abstractive summarization
Gliwa, B., Mochol, I., Biesek, M., and Wawer, A. (2019) · 1911
Earlier work this paper cites.
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P. (2019) · 1911
Earlier work this paper cites.
Improving n-gram language models with pre-trained deep transformer
Wang, Y., Huang, H., Liu, Z., Pang, Y., Wang, Y., Zhai, C., and Peng, F. (2019) · 1911
Earlier work this paper cites.
Plug-and-play diffusion features for text-driven image-to-image translation
Tumanyan, N., Geyer, M., Bagon, S., and Dekel, T. (2023) · 1930
Earlier work this paper cites.
Multi-concept customization of text-to-image diffusion
Kumari, N., Zhang, B., Zhang, R., Shechtman, E., and Zhu, J.-Y. (2023) · 1941
Earlier work this paper cites.
A generalized loss function for crowd counting and localization
Wan, J., Liu, Z., and Chan, A. B. (2021) · 1983
Earlier work this paper cites.
Class-based n-gram models of natural language
Brown, P. F., Della Pietra, V. J., Desouza, P. V., Lai, J. C., and Mercer, R. L. (1992) · 1992
Earlier work this paper cites.
A weighted average n-gram model of natural language
O’Boyle, P., Owens, M., and Smith, F. J. (1994) · 1994
Earlier work this paper cites.
Newsweeder: Learning to filter netnews
Lang, K. (1995) · 1995
Earlier work this paper cites.
The feret database and evaluation procedure for face-recognition algorithms
Phillips, P. J., Wechsler, H., Huang, J., and Rauss, P. J. (1998) · 1998
Earlier work this paper cites.
The trec-8 question answering track evaluation
Voorhees, E. M., Tice, D. M., et al. (1999) · 1999
Earlier work this paper cites.
A neural probabilistic language model
Bengio, Y., Ducharme, R., and Vincent, P. (2000) · 2000
Earlier work this paper cites.
Two decades of statistical language modeling: Where do we go from here?
Rosenfeld, R. (2000) · 2000
Earlier work this paper cites.
Learning question classifiers
Li, X. and Roth, D. (2002) · 2002
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (2002) · 2002
Earlier work this paper cites.
Efficient intent detection with dual sentence encoders
Casanueva, I., Temčinas, T., Gerz, D., Henderson, M., and Vulić, I. (2020) · 2003
Earlier work this paper cites.
Data augmentation using pre-trained transformer models
Kumar, V., Choudhary, A., and Cho, E. (2020) · 2003
Earlier work this paper cites.
Introduction to the conll-2003 shared task: Language-independent named entity recognition
Sang, E. F. and De Meulder, F. (2003) · 2003
Earlier work this paper cites.
Introduction to the special issue on statistical language modeling
Gao, J. and Lin, C.-Y. (2004) · 2004
Earlier work this paper cites.
Mining and summarizing customer reviews
Hu, M. and Liu, B. (2004) · 2004
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Lin, C.-Y. (2004) · 2004
Earlier work this paper cites.
A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Pang, B. and Lee, L. (2004) · 2004
Earlier work this paper cites.
Recipes for building an open-domain chatbot
Roller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., Xu, J., Ott, M., Shuster, K., Smith, E. M., et al. (2020) · 2004
Earlier work this paper cites.
An analysis of the relative hardness of reuters-21578 subsets
Debole, F. and Sebastiani, F. (2005) · 2005
Earlier work this paper cites.
Statistical language modeling for information retrieval
Liu, X. and Croft, W. B. (2005) · 2005
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Pang, B. and Lee, L. (2005) · 2005
Earlier work this paper cites.
Annotating expressions of opinions and emotions in language
Wiebe, J., Wilson, T., and Cardie, C. (2005) · 2005
Earlier work this paper cites.
Practical solutions to the problem of diagonal dominance in kernel document clustering
Greene, D. and Cunningham, P. (2006) · 2006
Earlier work this paper cites.
N-gram-based machine translation
Marino, J. B., Banchs, R. E., Crego, J. M., de Gispert, A., Lambert, P., Fonollosa, J. A., and Costa-jussà, M. R. (2006) · 2006
Earlier work this paper cites.
In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D. (2020) · 2007
Earlier work this paper cites.
Deep transformer based data augmentation with subword units for morphologically rich online asr
Tarján, B., Szaszák, G., Fegyó, T., and Mihajlik, P. (2020) · 2007
Earlier work this paper cites.
Iemocap: Interactive emotional dyadic motion capture database
Busso, C., Bulut, M., Lee, C.-C., Kazemzadeh, A., Mower, E., Kim, S., Chang, J. N., Lee, S., and Narayanan, S. S. (2008) · 2008
Earlier work this paper cites.
Multilingual translation with extensible multilingual pretraining and finetuning
Tang, Y., Tran, C., Li, X., Chen, P.-J., Goyal, N., Chaudhary, V., Gu, J., and Fan, A. (2020) · 2008
Earlier work this paper cites.
Accelerating real-time question answering via question generation
Fang, Y., Wang, S., Gan, Z., Sun, S., Liu, J., and Zhu, C. (2020) · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al. (2009) · 2009
Earlier work this paper cites.
Policyqa: A reading comprehension dataset for privacy policies
Ahmad, W. U., Chi, J., Tian, Y., and Chang, K.-W. (2020) · 2010
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A. (2010) · 2010
Earlier work this paper cites.
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Cernockỳ, J., and Khudanpur, S. (2010) · 2010
Earlier work this paper cites.
Thakur, N., Reimers, N., Daxenberger, J., and Gurevych, I. (2020) · 2010
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Maas, A., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., and Potts, C. (2011) · 2011
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B. (2020) · 2011
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S. (2011) · 2011
Earlier work this paper cites.
Making pre-trained language models better few-shot learners
Gao, T., Fisch, A., and Chen, D. (2020) · 2012
Earlier work this paper cites.
Fencebox: A platform for defeating adversarial examples with data augmentation techniques
Qiu, H., Zeng, Y., Zhang, T., Jiang, Y., and Qiu, M. (2020) · 2012
Earlier work this paper cites.
Lstm neural networks for language modeling
Sundermeyer, M., Schlüter, R., and Ney, H. (2012) · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2013) · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., and Potts, C. (2013) · 2013
Earlier work this paper cites.
Meteor universal: Language specific translation evaluation for any target language
Denkowski, M. and Lavie, A. (2014) · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C. (2014) · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014) · 2014
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Bowman, S. R., Angeli, G., Potts, C., and Manning, C. D. (2015) · 2015
Earlier work this paper cites.
Bidirectional lstm-crf models for sequence tagging
Huang, Z., Xu, W., and Yu, K. (2015) · 2015
Earlier work this paper cites.
chrf: character n-gram f-score for automatic mt evaluation
Popović, M. (2015) · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S. (2015) · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J. (2015) · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
Earlier work this paper cites.
An overview of the bioasq large-scale biomedical semantic indexing and question answering competition
Tsatsaronis, G., Balikas, G., Malakasiotis, P., Partalas, I., Zschunke, M., Alvers, M. R., Weissenborn, D., Krithara, A., Petridis, S., Polychronopoulos, D., et al. (2015) · 2015
Earlier work this paper cites.
Cider: Consensus-based image description evaluation
Vedantam, R., Lawrence Zitnick, C., and Parikh, D. (2015) · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
Zhang, X., Zhao, J., and LeCun, Y. (2015) · 2015
Earlier work this paper cites.
Spice: Semantic propositional image caption evaluation
Anderson, P., Fernando, B., Johnson, M., and Gould, S. (2016) · 2016
Earlier work this paper cites.
Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
He, R. and McAuley, J. (2016) · 2016
Earlier work this paper cites.
Adversarial training methods for semi-supervised text classification
Miyato, T., Dai, A. M., and Goodfellow, I. (2016) · 2016
Earlier work this paper cites.
Counter-fitting word vectors to linguistic constraints
Mrkšić, N., Séaghdha, D. O., Thomson, B., Gašić, M., Rojas-Barahona, L., Su, P.-H., Vandyke, D., Wen, T.-H., and Young, S. (2016) · 2016
Earlier work this paper cites.
Squad: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P. (2016) · 2016
Earlier work this paper cites.
Single-image crowd counting via multi-column convolutional neural network
Zhang, Y., Zhou, D., Chen, S., Gao, S., and Ma, Y. (2016) · 2016
Earlier work this paper cites.
Data augmentation generative adversarial networks
Antoniou, A., Storkey, A., and Edwards, H. (2017) · 2017
Earlier work this paper cites.
PubMed 200k RCT: a dataset for sequential sentence classification in medical abstracts
Dernoncourt, F. and Lee, J. Y. (2017) · 2017
Earlier work this paper cites.
Neural collaborative filtering
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.-S. (2017) · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017) · 2017
Earlier work this paper cites.
Adversarial examples for evaluating reading comprehension systems
Jia, R. and Liang, P. (2017) · 2017
Earlier work this paper cites.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Joshi, M., Choi, E., Weld, D. S., and Zettlemoyer, L. (2017) · 2017
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Rotational data augmentation for electroencephalographic data
Krell, M. M. and Kim, S. K. (2017) · 2017
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Deeper, broader and artier domain generalization
Li, D., Yang, Y., Song, Y.-Z., and Hospedales, T. M. (2017) · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. (2017) · 2017
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A respiratory sound database for the development of automated classification
Rocha, B., Filos, D., Mendes, L., Vogiatzis, I., Perantoni, E., Kaimakamis, E., Natsiavas, P., Oliveira, A., Jácome, C., Marques, A., et al. (2018) · 2017
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Dagam: Data augmentation with generation and modification
Jo, B.-C., Heo, T.-S., Park, Y., Yoo, Y., Cho, W. I., and Kim, K. (2022) · 2022
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The devil is in the details: Whole slide image acquisition and processing for artifacts detection, color variation, and data augmentation: A review
Kanwal, N., Pérez-Bueno, F., Schmidt, A., Engan, K., and Molina, R. (2022) · 2022
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Skillbot: Towards data augmentation using transformer language model and linguistic evaluation
Khatri, S., Iqbal, M., Ubakanma, G., and van der Vliet-Firth, S. (2022) · 2022
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Diffusionclip: Text-guided diffusion models for robust image manipulation
Kim, G., Kwon, T., and Ye, J. C. (2022) · 2022
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Wanli: Worker and ai collaboration for natural language inference dataset creation
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Data augmentation for morphological reinflection
Silfverberg, M., Wiemerslage, A., Liu, L., and Mao, L. J. (2017) · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017) · 2017
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Deep hashing network for unsupervised domain adaptation
Venkateswara, H., Eusebio, J., Chakraborty, S., and Panchanathan, S. (2017) · 2017
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A broad-coverage challenge corpus for sentence understanding through inference
Williams, A., Nangia, N., and Bowman, S. R. (2017) · 2017
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D. (2017) · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A. (2017) · 2017
Cited alongside, same era.
Generating natural language adversarial examples
Alzantot, M., Sharma, Y., Elgohary, A., Ho, B.-J., Srivastava, M., and Chang, K.-W. (2018) · 2018
Cited alongside, same era.
Liu, A., Swayamdipta, S., Smith, N. A., and Choi, Y. (2022) · 2022
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Generating training data with language models: Towards zero-shot language understanding
Meng, Y., Huang, J., Zhang, Y., and Han, J. (2022) · 2022
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Text and code embeddings by contrastive pre-training
Neelakantan, A., Xu, T., Puri, R., Radford, A., Han, J. M., Tworek, J., Yuan, Q., Tezak, N., Kim, J. W., Hallacy, C., et al. (2022) · 2022
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Segmentation assisted u-shaped multi-scale transformer for crowd counting
Qian, Y., Zhang, L., Hong, X., Donovan, C., Arandjelovic, O., Fife, U., and Harbin, P. (2022) · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022) · 2022
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Max pooling with vision transformers reconciles class and shape in weakly supervised semantic segmentation
Rossetti, S., Zappia, D., Sanzari, M., Schaerf, M., and Pirri, F. (2022) · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al. (2022) · 2022
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Data augmentation for intent classification with off-the-shelf large language models
Sahu, G., Rodriguez, P., Laradji, I. H., Atighehchian, P., Vazquez, D., and Bahdanau, D. (2022) · 2022
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Unifying language learning paradigms
Tay, Y., Dehghani, M., Tran, V. Q., Garcia, X., Bahri, D., Schuster, T., Zheng, H. S., Houlsby, N., and Metzler, D. (2022) · 2022
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Sentence-aware adversarial meta-learning for few-shot text classification
Wang, S., Liu, X., Liu, B., and Dong, D. (2022) · 2022
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The sjtu system for dcase2022 challenge task 6: Audio captioning with audiotext retrieval pre-training
Xu, X., Xie, Z., Wu, M., and Yu, K. (2022) · 2022
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Image data augmentation for deep learning: A survey
Yang, S., Xiao, W., Zhang, M., Guo, S., Zhao, J., and Shen, F. (2022) · 2022
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Coca: Contrastive captioners are image-text foundation models
Yu, J., Wang, Z., Vasudevan, V., Yeung, L., Seyedhosseini, M., and Wu, Y. (2022) · 2022
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Tip-adapter: Training-free adaption of clip for few-shot classification
Zhang, R., Zhang, W., Fang, R., Gao, P., Li, K., Dai, J., Qiao, Y., and Li, H. (2022) · 2022
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Can audio captions be evaluated with image caption metrics?
Zhou, Z., Zhang, Z., Xu, X., Xie, Z., Wu, M., and Zhu, K. Q. (2022b) · 2022
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Promptmix: Text-to-image diffusion models enhance the performance of lightweight networks
Bakhtiarnia, A., Zhang, Q., and Iosifidis, A. (2023) · 2023
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Instructpix2pix: Learning to follow image editing instructions
Brooks, T., Holynski, A., and Efros, A. A. (2023) · 2023
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Cai, X., Xiao, M., Ning, Z., and Zhou, Y. (2023) · 2023
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Generative data augmentation using llms improves distributional robustness in question answering
Chowdhury, A. G. and Chadha, A. (2023) · 2023
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Free dolly: Introducing the world’s first truly open instruction-tuned llm
Conover, M., Hayes, M., Mathur, A., Meng, X., Xie, J., Wan, J., Shah, S., Ghodsi, A., Wendell, P., Zaharia, M., et al. (2023) · 2023
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Chataug: Leveraging chatgpt for text data augmentation
Dai, H., Liu, Z., Liao, W., Huang, X., Wu, Z., Zhao, L., Liu, W., Liu, N., Li, S., Zhu, D., et al. (2023) · 2023
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Semantic generative augmentations for few-shot counting
Doubinsky, P., Audebert, N., Crucianu, M., and Borgne, H. L. (2023) · 2023
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Boosting dermatoscopic lesion segmentation via diffusion models with visual and textual prompts
Du, S., Wang, X., Lu, Y., Zhou, Y., Zhang, S., Yuille, A., Li, K., and Zhou, Z. (2023) · 2023
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Diversify your vision datasets with automatic diffusion-based augmentation
Dunlap, L., Umino, A., Zhang, H., Yang, J., Gonzalez, J. E., and Darrell, T. (2023) · 2023
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Expressive text-to-image generation with rich text
Ge, S., Park, T., Zhu, J.-Y., and Huang, J.-B. (2023) · 2023
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Steerer: Resolving scale variations for counting and localization via selective inheritance learning
Han, T., Bai, L., Liu, L., and Ouyang, W. (2023) · 2023
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Mixgen: A new multi-modal data augmentation
Hao, X., Zhu, Y., Appalaraju, S., Zhang, A., Zhang, W., Li, B., and Li, M. (2023) · 2023
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Cross domain generative augmentation: Domain generalization with latent diffusion models
Hemati, S., Beitollahi, M., Estiri, A. H., Omari, B. A., Chen, X., and Zhang, G. (2023) · 2023
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Reversion: Diffusion-based relation inversion from images
Huang, Z., Wu, T., Jiang, Y., Chan, K. C., and Liu, Z. (2023) · 2023
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YOLO by Ultralytics
Jocher, G., Chaurasia, A., and Qiu, J. (2023) · 2023
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Text data augmentation in low-resource settings via fine-tuning of large language models
Kaddour, J. and Liu, Q. (2023) · 2023
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Imagic: Text-based real image editing with diffusion models
Kawar, B., Zada, S., Lang, O., Tov, O., Chang, H., Dekel, T., Mosseri, I., and Irani, M. (2023) · 2023
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Adversarial fine-tuning using generated respiratory sound to address class imbalance
Kim, J.-W., Yoon, C., Toikkanen, M., Bae, S., and Jung, H.-Y. (2023) · 2023
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Natural language dataset generation framework for visualizations powered by large language models
Ko, H.-K., Jeon, H., Park, G., Kim, D. H., Kim, N. W., Kim, J., and Seo, J. (2023) · 2023
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Genie: Generative hard negative images through diffusion
Koohpayegani, S. A., Singh, A., Navaneet, K., Jamali-Rad, H., and Pirsiavash, H. (2023) · 2023
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Advanced data augmentation approaches: A comprehensive survey and future directions
Kumar, T., Turab, M., Raj, K., Mileo, A., Brennan, R., and Bendechache, M. (2023) · 2023
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Can large language models aid in annotating speech emotional data? uncovering new frontiers
Latif, S., Usama, M., Malik, M. I., and Schuller, B. W. (2023) · 2023
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Epa: Easy prompt augmentation on large language models via multiple sources and multiple targets
Lu, H. and Lam, W. (2023) · 2023
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Wovogen: World volume-aware diffusion for controllable multi-camera driving scene generation
Lu, J., Huang, Z., Zhang, J., Yang, Z., and Zhang, L. (2023) · 2023
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Camdiff: Camouflage image augmentation via diffusion model
Luo, X.-J., Wang, S., Wu, Z., Sakaridis, C., Cheng, Y., Fan, D.-P., and Van Gool, L. (2023) · 2023
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Unified multi-modal latent diffusion for joint subject and text conditional image generation
Ma, Y., Yang, H., Wang, W., Fu, J., and Liu, J. (2023) · 2023
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Tuning language models as training data generators for augmentation-enhanced few-shot learning
Meng, Y., Michalski, M., Huang, J., Zhang, Y., Abdelzaher, T., and Han, J. (2023) · 2023
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Visual instruction inversion: Image editing via visual prompting
Nguyen, T., Li, Y., Ojha, U., and Lee, Y. J. (2023) · 2023
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Data augmentation for neural machine translation using generative language model
Oh, S., Jung, W., et al. (2023) · 2023
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Localizing object-level shape variations with text-to-image diffusion models
Patashnik, O., Garibi, D., Azuri, I., Averbuch-Elor, H., and Cohen-Or, D. (2023) · 2023
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., and Aberman, K. (2023) · 2023
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Iclef: In-context learning with expert feedback for explainable style transfer
Saakyan, A. and Muresan, S. (2023) · 2023
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Catfood: Counterfactual augmented training for improving out-of-domain performance and calibration
Sachdeva, R., Tutek, M., and Gurevych, I. (2023) · 2023
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Schlegel, V., Li, H., Wu, Y., Subramanian, A., Nguyen, T.-T., Kashyap, A. R., Beck, D., Zeng, X., Batista-Navarro, R. T., Winkler, S., et al. (2023) · 2023
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Generative data augmentation improves scribble-supervised semantic segmentation
Schnell, J., Wang, J., Qi, L., Hu, V. T., and Tang, M. (2023) · 2023
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Diffuseexpand: Expanding dataset for 2d medical image segmentation using diffusion models
Shao, S., Yuan, X., Huang, Z., Qiu, Z., Wang, S., and Zhou, K. (2023) · 2023
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Augmenting text for spoken language understanding with large language models
Sharma, R., Kim, S., Lazar, D., Le, T., Shrivastava, A., Ahn, K., Kansal, P., Sari, L., Kalinli, O., and Seltzer, M. (2023) · 2023
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Instantbooth: Personalized text-to-image generation without test-time finetuning
Shi, J., Xiong, W., Lin, Z., and Jung, H. J. (2023) · 2023
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Towards expert-level medical question answering with large language models
Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., Neal, D., et al. (2023) · 2023
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Stablelm: Stability ai language models
Stability AI (2023) · 2023
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Imagebrush: Learning visual in-context instructions for exemplar-based image manipulation
Sun, Y., Yang, Y., Peng, H., Shen, Y., Yang, Y., Hu, H., Qiu, L., and Koike, H. (2023) · 2023
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Just-in-time security patch detection–llm at the rescue for data augmentation
Tang, X., Chen, Z., Kim, K., Tian, H., Ezzini, S., and Klein, J. (2023) · 2023
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Stanford alpaca: An instruction-following llama model
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B. (2023) · 2023
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Effective data augmentation with diffusion models
Trabucco, B., Doherty, K., Gurinas, M., and Salakhutdinov, R. (2023) · 2023
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Zeroshotdataaug: Generating and augmenting training data with chatgpt
Ubani, S., Polat, S. O., and Nielsen, R. (2023) · 2023
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The big data myth: Using diffusion models for dataset generation to train deep detection models
Voetman, R., Aghaei, M., and Dijkstra, K. (2023) · 2023
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Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation
Wei, Y., Zhang, Y., Ji, Z., Bai, J., Zhang, L., and Zuo, W. (2023) · 2023
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Fastcomposer: Tuning-free multi-subject image generation with localized attention
Xiao, G., Yin, T., Freeman, W. T., Durand, F., and Han, S. (2023) · 2023
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Smartbrush: Text and shape guided object inpainting with diffusion model
Xie, S., Zhang, Z., Lin, Z., Hinz, T., and Zhang, K. (2023) · 2023
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Diffusion models: A comprehensive survey of methods and applications
Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., and Yang, M.-H. (2023) · 2023
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Overview of the mediqa-sum task at imageclef 2023: Summarization and classification of doctor-patient conversations
Yim, W., Ben Abacha, A., Snider, N., Adams, G., and Yetisgen, M. (2023) · 2023
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Ttida: Controllable generative data augmentation via text-to-text and text-to-image models
Yin, Y., Kaddour, J., Zhang, X., Nie, Y., Liu, Z., Kong, L., and Liu, Q. (2023) · 2023
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Diffusion-based data augmentation for nuclei image segmentation
Yu, X., Li, G., Lou, W., Liu, S., Wan, X., Chen, Y., and Li, H. (2023) · 2023
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Boosting unsupervised contrastive learning using diffusion-based data augmentation from scratch
Zang, Z., Luo, H., Wang, K., Zhang, P., Wang, F., Li, S., You, Y., et al. (2023) · 2023
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A survey of large language models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al. (2023) · 2023
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Augesc: Dialogue augmentation with large language models for emotional support conversation
Zheng, C., Sabour, S., Wen, J., Zhang, Z., and Huang, M. (2023) · 2023
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Text-to-image diffusion models are zero shot classifiers
Clark, K. and Jaini, P. (2024) · 2024
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Erfanian, M., Jagadish, H., and Asudeh, A. (2024) · 2024
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Instagen: Enhancing object detection by training on synthetic dataset
Feng, C., Zhong, Y., Jie, Z., Xie, W., and Ma, L. (2024) · 2024
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Llm-da: Data augmentation via large language models for few-shot named entity recognition
Ye, J., Xu, N., Wang, Y., Zhou, J., Zhang, Q., Gui, T., and Huang, X. (2024) · 2024
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Panoptic feature fusion net: a novel instance segmentation paradigm for biomedical and biological images
Liu, D., Zhang, D., Song, Y., Huang, H., and Cai, W. (2021a) · 2059
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