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The success of pretrain-finetune paradigm brings about the release of numerous model weights.
On the mathematical foundations of theoretical statistics
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The mnist database of handwritten digits
Y. LeCun · 1998
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Bleu: a method for automatic evaluation of machine translation
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu · 2002
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Rouge: A package for automatic evaluation of summaries
C.-Y. Lin · 2004
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
S. Banerjee and A. Lavie · 2005
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Automatically constructing a corpus of sentential paraphrases
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The third pascal recognizing textual entailment challenge
D. Giampiccolo, B. Magnini, I. Dagan, and W. B. Dolan · 2007
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Visualizing data using t-sne
L. van der Maaten and G. Hinton · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
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Sun database: Large-scale scene recognition from abbey to zoo
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba · 2010
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An analysis of single-layer networks in unsupervised feature learning
A. Coates, A. Ng, and H. Lee · 2011
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Novel dataset for fine-grained image categorization
A. Khosla, N. Jayadevaprakash, B. Yao, and L. Fei-Fei · 2011
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Choice of plausible alternatives: An evaluation of commonsense causal reasoning
M. Roemmele, C. A. Bejan, and A. S. Gordon · 2011
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The german traffic sign recognition benchmark: a multi-class classification competition
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel · 2011
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The caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Reading digits in natural images with unsupervised feature learning
N. Yuval · 2011
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The winograd schema challenge
H. Levesque, E. Davis, and L. Morgenstern · 2012
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Cats and dogs
O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. Jawahar · 2012
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Dogs vs. cats, 2013
W. Cukierski · 2013
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3d object representations for fine-grained categorization
J. Krause, M. Stark, J. Deng, and L. Fei-Fei · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts · 2013
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Food-101 – mining discriminative components with random forests
L. Bossard, M. Guillaumin, and L. Van Gool · 2014
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Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi · 2014
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Cider: Consensus-based image description evaluation
R. Vedantam, C. Lawrence Zitnick, and D. Parikh · 2015
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M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
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Torchvision: Pytorch’s computer vision library
T. maintainers and contributors · 2016
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Squad: 100,000+ questions for machine comprehension of text
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang · 2016
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Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation
D. Cer, M. Diab, E. Agirre, I. Lopez-Gazpio, and L. Specia · 2017
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Remote sensing image scene classification: Benchmark and state of the art
G. Cheng, J. Han, and X. Lu · 2017
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Emnist: Extending mnist to handwritten letters
G. Cohen, S. Afshar, J. Tapson, and A. Van Schaik · 2017
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Y. Goyal, T. Khot, D. Summers-Stay, D. Batra, and D. Parikh · 2017
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First quora dataset release: Question pairs. data. quora. com
S. Iyer, N. Dandekar, K. Csernai, et al · 2017
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Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection
K. Pogorelov, K. R. Randel, C. Griwodz, S. L. Eskeland, T. de Lange, D. Johansen, C. Spampinato, D.-T. Dang-Nguyen, M. Lux, P. T. Schmidt, et al · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
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Attention is all you need
Bean disease dataset, January 2020
M. A. Lab · 2020
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Dcnn-based vegetable image classification using transfer learning: A comparative study
M. I. Ahmed, S. M. Mamun, and A. U. Z. Asif · 2021
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On the opportunities and risks of foundation models
R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill, et al · 2021
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Lora: Low-rank adaptation of large language models
E. J. Hu, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen, et al · 2021
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Learning transferable visual models from natural language supervision
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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A broad-coverage challenge corpus for sentence understanding through inference
A. Williams, N. Nangia, and S. R. Bowman · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Garbage classification
CCHANG · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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Fruit recognition from images using deep learning
H. Muresan and M. Oltean · 2018
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Wic: the word-in-context dataset for evaluating context-sensitive meaning representations
M. T. Pilehvar and J. Camacho-Collados · 2018
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K. Sakaguchi, R. L. Bras, C. Bhagavatula, and Y. Choi · 2021
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Multitask prompted training enables zero-shot task generalization
V. Sanh, A. Webson, C. Raffel, S. H. Bach, L. Sutawika, Z. Alyafeai, A. Chaffin, A. Stiegler, T. L. Scao, A. Raja, et al · 2021
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Multi-task learning for dense prediction tasks: A survey
S. Vandenhende, S. Georgoulis, W. Van Gansbeke, M. Proesmans, D. Dai, and L. Van Gool · 2021
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Classification of weather phenomenon from images by using deep convolutional neural network
H. Xiao, F. Zhang, Z. Shen, K. Wu, and J. Zhang · 2021
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A survey on multi-task learning
Y. Zhang and Q. Yang · 2021
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Editing models with task arithmetic
G. Ilharco, M. T. Ribeiro, M. Wortsman, L. Schmidt, H. Hajishirzi, and A. Farhadi · 2022
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Dataless knowledge fusion by merging weights of language models
X. Jin, X. Ren, D. Preotiuc-Pietro, and P. Cheng · 2022
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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
H. Liu, D. Tam, M. Muqeeth, J. Mohta, T. Huang, M. Bansal, and C. A. Raffel · 2022
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Merging models with fisher-weighted averaging
M. S. Matena and C. A. Raffel · 2022
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Vision transformers are robust learners
S. Paul and P.-Y. Chen · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
M. Wortsman, G. Ilharco, S. Y. Gadre, R. Roelofs, R. Gontijo-Lopes, A. S. Morcos, H. Namkoong, A. Farhadi, Y. Carmon, S. Kornblith, et al · 2022
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b-darts: Beta-decay regularization for differentiable architecture search
P. Ye, B. Li, Y. Li, T. Chen, J. Fan, and W. Ouyang · 2022
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Stimulative training of residual networks: A social psychology perspective of loafing
P. Ye, S. Tang, B. Li, T. Chen, and W. Ouyang · 2022
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Mangoleafbd: A comprehensive image dataset to classify diseased and healthy mango leaves
S. I. Ahmed, M. Ibrahim, M. Nadim, M. M. Rahman, M. M. Shejunti, T. Jabid, and M. S. Ali · 2023
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Model ratatouille: Recycling diverse models for out-of-distribution generalization
A. Ramé, K. Ahuja, J. Zhang, M. Cord, L. Bottou, and D. Lopez-Paz · 2023
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Zipit! merging models from different tasks without training
G. Stoica, D. Bolya, J. Bjorner, T. Hearn, and J. Hoffman · 2023
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Image as a foreign language: BEiT pretraining for vision and vision-language tasks
W. Wang, H. Bao, L. Dong, J. Bjorck, Z. Peng, Q. Liu, K. Aggarwal, O. K. Mohammed, S. Singhal, S. Som, and F. Wei · 2023
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Ties-merging: Resolving interference when merging models
P. Yadav, D. Tam, L. Choshen, C. Raffel, and M. Bansal · 2023
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Adamerging: Adaptive model merging for multi-task learning
E. Yang, Z. Wang, L. Shen, S. Liu, G. Guo, X. Wang, and D. Tao · 2023
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Stimulative training++: Go beyond the performance limits of residual networks
P. Ye, T. He, S. Tang, B. Li, T. Chen, L. Bai, and W. Ouyang · 2023
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Merging vision transformers from different tasks and domains
P. Ye, C. Huang, M. Shen, T. Chen, Y. Huang, Y. Zhang, and W. Ouyang · 2023
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Language models are super mario: Absorbing abilities from homologous models as a free lunch
L. Yu, B. Yu, H. Yu, F. Huang, and Y. Li · 2023
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Uni3d: A unified baseline for multi-dataset 3d object detection
B. Zhang, J. Yuan, B. Shi, T. Chen, Y. Li, and Y. Qiao · 2023
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Composing parameter-efficient modules with arithmetic operation
J. Zhang, J. Liu, J. He, et al · 2023
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Arcee’s mergekit: A toolkit for merging large language models
C. Goddard, S. Siriwardhana, M. Ehghaghi, L. Meyers, V. Karpukhin, B. Benedict, M. McQuade, and J. Solawetz · 2024
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FusionBench: A Comprehensive Benchmark of Deep Model Fusion, June 2024
A. Tang, L. Shen, Y. Luo, H. Hu, B. Du, and D. Tao · 2024
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