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Unsupervised Contrastive learning has gained prominence in fields such as vision, and biology, leveraging predefined positive/negative samples for representation learning.
Data augmentation using gans, 2019
Tanaka, F. H. K. d. S. and Aranha, C · 1904
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Momentum Contrast for Unsupervised Visual Representation Learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 1911
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A Simple Framework for Contrastive Learning of Visual Representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2002
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Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., Piot, B., Kavukcuoglu, K., Munos, R., and Valko, M · 2006
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Visualizing data using t-sne
Van der Maaten, L. and Hinton, G · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Parametric UMAP embeddings for representation and semi-supervised learning
Sainburg, T., McInnes, L., and Gentner, T. Q · 2009
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Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions, 2010
Halko, N., Martinsson, P.-G., and Tropp, J. A · 2010
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Genomics for the world
Bustamante, C. D., De La Vega, F. M., and Burchard, E. G · 2011
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An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A., and Lee, H · 2011
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Theory and use of the em algorithm
Gupta, M. R., Chen, Y., et al · 2011
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Tiny imagenet visual recognition challenge
Le, Y. and Yang, X · 2015
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The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins
Rouillard, A. D., Gundersen, G. W., Fernandez, N. F., Wang, Z., Monteiro, C. D., McDermott, M. G., and Ma’ayan, A · 2016
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Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data
Weber, L. M. and Robinson, M. D · 2016
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Data augmentation generative adversarial networks, 2017
Antoniou, A., Storkey, A., and Edwards, H · 2017
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A bayesian data augmentation approach for learning deep models
Tran, T., Pham, T., Carneiro, G., Palmer, L. J., and Reid, I. D · 2017
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mixup: Beyond empirical risk minimization
Zhang, H., Cissé, M., Dauphin, Y. N., and Lopez-Paz, D · 2017
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Unlabeled samples generated by GAN improve the person re-identification baseline in vitro
Zheng, Z., Zheng, L., and Yang, Y · 2017
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Mapping the mouse cell atlas by microwell-seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al · 2018
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Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
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Classifier training from a generative model
Dat, P. T., Dutt, A., Pellerin, D., and Quénot, G · 2019
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Progan: Protein solubility generative adversarial nets for data augmentation in dnn framework
Han, X., Zhang, L., Zhou, K., and Wang, X · 2019
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Visualizing structure and transitions in high dimensional biological data
Moon, K. R. and van Dijk · 2019
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Moor, M., Horn, M., Rieck, B., and Borgwardt, K · 2019
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Generating diverse high-fidelity images with VQ-VAE-2
Razavi, A., van den Oord, A., and Vinyals, O · 2019
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Single cell transcriptomics comes of age
Aldridge, S. and Teichmann, S. A · 2020
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This dataset does not exist: Training models from generated images
Besnier, V., Jain, H., Bursuc, A., Cord, M., and Pérez, P · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Cubuk, E. D., Zoph, B., Shlens, J., and Le, Q. V · 2020
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Construction of a human cell landscape at single-cell level
Han, X., Zhou, Z., Fei, L., Sun, H., Wang, R., Chen, Y., Chen, H., Wang, J., Tang, H., Ge, W., et al · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
What makes for good views for contrastive learning?
Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., and Isola, P · 2020
Cited alongside, same era.
Fine-grained image-to-image transformation towards visual recognition
Xiong, W., He, Y., Zhang, Y., Luo, W., Ma, L., and Luo, J · 2020
Crafting better contrastive views for siamese representation learning
Peng, X., Wang, K., Zhu, Z., Wang, M., and You, Y · 2022
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Hierarchical text-conditional image generation with clip latents, 2022
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 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
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Photorealistic text-to-image diffusion models with deep language understanding, 2022
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., Salimans, T., Ho, J., Fleet, D. J., and Norouzi, M · 2022
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Hierarchical nearest neighbor graph embedding for efficient dimensionality reduction
Sarfraz, S., Koulakis, M., Seibold, C., and Stiefelhagen, R · 2022
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Effective data augmentation with multi-domain learning gans
Yamaguchi, S., Kanai, S., and Eda, T · 2020
Cited alongside, same era.
Exploring simple siamese representation learning
Chen, X. and He, K · 2021
Cited alongside, same era.
Parametric contrastive learning
Cui, J., Zhong, Z., Liu, S., Yu, B., and Jia, J · 2021
Cited alongside, same era.
Flattening sharpness for dynamic gradient projection memory benefits continual learning
Deng, D., Chen, G., Hao, J., Wang, Q., and Heng, P.-A · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A. Q · 2021
Cited alongside, same era.
EC-GAN: low-sample classification using semi-supervised algorithms and gans (student abstract)
Haque, A · 2021
Cited alongside, same era.
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Laion-5b: An open large-scale dataset for training next generation image-text models, 2022
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., Schramowski, P., Kundurthy, S., Crowson, K., Schmidt, L., Kaczmarczyk, R., and Jitsev, J · 2022
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Artificial intelligence defines protein-based classification of thyroid nodules
Sun, Y., Selvarajan, S., Zang, Z., Liu, W., Zhu, Y., Zhang, H., Chen, W., Chen, H., Li, L., Cai, X., et al · 2022
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Contrastive learning with stronger augmentations
Wang, X. and Qi, G.-J · 2022
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Understanding how dimension reduction tools work: An empirical approach to deciphering t-sne, umap, trimap, and pacmap for data visualization
Wang, Y., Huang, H., Rudin, C., and Shaposhnik, Y · 2022
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Rgb color model aware computational color naming and its application to data augmentation
Yan, Z., Xu, L., Suzuki, A., Wang, J., Cao, J., and Huang, J · 2022
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Rethinking the augmentation module in contrastive learning: Learning hierarchical augmentation invariance with expanded views
Zhang, J. and Ma, K · 2022
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M-mix: Generating hard negatives via multi-sample mixing for contrastive learning
Zhang, S., Liu, M., Yan, J., Zhang, H., Huang, L., Yang, X., and Lu, P · 2022
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The role of ai in drug discovery: challenges, opportunities, and strategies
Blanco-Gonzalez, A., Cabezon, A., Seco-Gonzalez, A., Conde-Torres, D., Antelo-Riveiro, P., Pineiro, A., and Garcia-Fandino, R · 2023
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The nucleotide transformer: Building and evaluating robust foundation models for human genomics
Dalla-Torre, H., Gonzalez, L., Mendoza-Revilla, J., Carranza, N. L., Grzywaczewski, A. H., Oteri, F., Dallago, C., Trop, E., de Almeida, B. P., Sirelkhatim, H., et al · 2023
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Dream the impossible: Outlier imagination with diffusion models, 2023
Du, X., Sun, Y., Zhu, X., and Li, Y · 2023
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Genomic benchmarks: a collection of datasets for genomic sequence classification
Grešová, K., Martinek, V., Čechák, D., Šimeček, P., and Alexiou, P · 2023
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Is synthetic data from generative models ready for image recognition?
He, R., Sun, S., Yu, X., Xue, C., Zhang, W., Torr, P., Bai, S., and Qi, X · 2023
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Evoaug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations
Lee, N. K., Tang, Z., Toneyan, S., and Koo, P. K · 2023
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Scorch: improving structure-based virtual screening with machine learning classifiers, data augmentation, and uncertainty estimation
McGibbon, M., Money-Kyrle, S., Blay, V., and Houston, D. R · 2023
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Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution
Nguyen, E., Poli, M., Faizi, M., Thomas, A. W., Wornow, M., Birch-Sykes, C., Massaroli, S., Patel, A., Rabideau, C. M., Bengio, Y., et al · 2023
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A primer on contrastive pretraining in language processing: Methods, lessons learned, and perspectives
Rethmeier, N. and Augenstein, I · 2023
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Transfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al · 2023
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Effective data augmentation with diffusion models
Trabucco, B., Doherty, K., Gurinas, M., and Salakhutdinov, R · 2023
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A comprehensive survey of image augmentation techniques for deep learning
Xu, M., Yoon, S., Fuentes, A., and Park, D. S · 2023
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Enzyme function prediction using contrastive learning
Yu, T., Cui, H., Li, J. C., Luo, Y., Jiang, G., and Zhao, H · 2023
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Boosting novel category discovery over domains with soft contrastive learning and all-in-one classifier
Zang, Z., Shang, L., Yang, S., Wang, F., Sun, B., Xie, X., and Li, S. Z · 2023
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Adaptive data augmentation for contrastive learning
Zhang, Y., Zhu, H., and Yu, S · 2023
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Maskdna-pgd: An innovative deep learning model for detecting dna methylation by integrating mask sequences and adversarial pgd training as a data augmentation method
Zheng, Z., Le, N. Q. K., and Chua, M. C. H · 2023
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Structure-preserving visualisation of high dimensional single-cell datasets
Szubert, B., Cole, J. E., Monaco, C., and Drozdov, I · 2045
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