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This work studies the relationship between Contrastive Learning and Domain Adaptation from a theoretical perspective.
Learning a similarity metric discriminatively, with application to face verification
Chopra, S., Hadsell, R., and LeCun, Y · 2005
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Measuring statistical dependence with hilbert-schmidt norms
Gretton, A., Bousquet, O., Smola, A., and Schölkopf, B · 2005
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A kernel method for the two-sample-problem
Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., and Smola, A · 2006
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Dimensionality reduction by learning an invariant mapping
Hadsell, R., Chopra, S., and LeCun, Y · 2006
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Distance metric learning for large margin nearest neighbor classification
Weinberger, K. and Saul, L · 2009
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Inbreast: Toward a full-field digital mammographic database
Moreira, I. C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M. J., and Cardoso, J. S · 2011
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The relationship between anatomic noise and volumetric breast density for digital mammography
Mainprize, J. G., Tyson, A. H., and Yaffe, M. J · 2012
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Transfer feature learning with joint distribution adaptation
Long, M., Wang, J., Ding, G., Sun, J., and Yu, P. S · 2013
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Patterns of dataset shift
Kull, M. and Flach, P · 2014
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., March, M., and Lempitsky, V · 2016
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SGDR: stochastic gradient descent with restarts
Loshchilov, I. and Hutter, F · 2016
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Breast cancer screening
on the Evaluation of Cancer-Preventive Interventions, I. W. G · 2016
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Improved deep metric learning with multi-class n-pair loss objective
Sohn, K · 2016
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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A curated mammography data set for use in computer-aided detection and diagnosis research
Lee, R., Gimenez, F., Hoogi, A., Miyake, K., Gorovoy, M., and Rubin, D · 2017
Earlier work this paper cites.
Deep transfer learning with joint adaptation networks
Long, M., Zhu, H., Wang, J., and Jordan, M. I · 2017
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Adversarial discriminative domain adaptation
Tzeng, E., Hoffman, J., Saenko, K., and Darrell, T · 2017
Earlier work this paper cites.
Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation
Damodaran, B. B., Kellenberger, B., Flamary, R., Tuia, D., and Courty, N · 2018
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Conditional adversarial domain adaptation
Long, M., Cao, Z., Wang, J., and Jordan, M. I · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
Cited alongside, same era.
Wasserstein distance guided representation learning for domain adaptation
Shen, J., Qu, Y., Zhang, W., and Yu, Y · 2018
Cited alongside, same era.
A review of domain adaptation without target labels
Kouw, W. M. and Loog, M · 2019
Cited alongside, same era.
Sliced wasserstein discrepancy for unsupervised domain adaptation
Lee, C.-Y., Batra, T., Baig, M. H., and Ulbricht, D · 2019
Cited alongside, same era.
Deep learning to improve breast cancer detection on screening mammography
Shen, L., Margolies, L. R., Rothstein, J. H., Fluder, E., McBride, R., and Sieh, W · 2019
Cited alongside, same era.
Deep neural networks improve radiologists’ performance in breast cancer screening
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Clda: Contrastive learning for semi-supervised domain adaptation
Singh, A · 2021
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Contrastive domain adaptation
Thota, M. and Leontidis, G · 2021
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A unified joint maximum mean discrepancy for domain adaptation, 2021
Wang, W., Li, B., Yang, S., Sun, J., Ding, Z., Chen, J., Dong, X., Wang, Z., and Li, H · 2021
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Federated contrastive learning for volumetric medical image segmentation
Wu, Y., Zeng, D., Wang, Z., Shi, Y., and Hu, J · 2021
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Domain generalization in deep learning based mass detection in mammography: A large-scale multi-center study
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Wu, N., Phang, J., Park, J., Shen, Y., Huang, Z., Zorin, M., Jastrzębski, S., Févray, T., Katsnelson, J., Kim, E., et al · 2019
Cited alongside, same era.
Contrastive learning of global and local features for medical image segmentation with limited annotations
Chaitanya, K., Erdil, E., Karani, N., and Konukoglu, E · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
Cited alongside, same era.
Supervised contrastive learning
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., and Krishnan, D · 2020
Cited alongside, same era.
Improving workflow efficiency for mammography using machine learning
Kyono, T., Gilbert, F. J., and van der Schaar, M · 2020
Cited alongside, same era.
Supervised contrastive pre-training for mammographic triage screening models
Cao, Z., Yang, Z., Tang, Y., Zhang, Y., Han, M., Xiao, J., Ma, J., and Chang, P · 2021
Cited alongside, same era.
Garrucho, L., Kushibar, K., Jouide, S., Diaz, O., Igual, L., and Lekadir, K · 2022
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Selective-supervised contrastive learning with noisy labels
Li, S., Xia, X., Ge, S., and Liu, T · 2022
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Breast cancer diagnosis in two-view mammography using end-to-end trained efficientnet-based convolutional network
Petrini, D. G., Shimizu, C., Roela, R. A., Valente, G. V., Folgueira, M. A. A. K., and Kim, H. Y · 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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A cookbook of self-supervised learning, 2023
Balestriero, R., Ibrahim, M., Sobal, V., Morcos, A., Shekhar, S., Goldstein, T., Bordes, F., Bardes, A., Mialon, G., Tian, Y., Schwarzschild, A., Wilson, A. G., Geiping, J., Garrido, Q., Fernandez, P., Bar, A., Pirsiavash, H., LeCun, Y., and Goldblum, M · 2023
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Attention-based deep learning system for classification of breast lesions—multimodal, weakly supervised approach
Bobowicz, M., Rygusik, M., Buler, J., Buler, R., Ferlin, M., Kwasigroch, A., Szurowska, E., and Grochowski, M · 2023
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From Theoretical to Practical Transfer Learning: The ADAPT Library , pp. 283–306
de Mathelin, A., Deheeger, F., Mougeot, M., and Vayatis, N · 2023
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Exploiting patch sizes and resolutions for multi-scale deep learning in mammogram image classification
Quintana, G. I., Li, Z., Vancamberg, L., Mougeot, M., Desolneux, A., and Muller, S · 2023
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Dacad: Domain adaptation contrastive learning for anomaly detection in multivariate time series
Darban, Z. Z., Yang, Y., Webb, G. I., Aggarwal, C. C., Wen, Q., and Salehi, M · 2024
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Hybrid feature mammogram analysis: detecting and localizing microcalcifications combining gabor, prewitt, glcm features, and top hat filtering enhanced with cnn architecture
Hernández-Vázquez, M. A., Hernández-Rodríguez, Y. M., Cortes-Rojas, F. D., Bayareh-Mancilla, R., and Cigarroa-Mayorga, O. E · 2024
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Llm2clip: Powerful language model unlock richer visual representation
Huang, W., Wu, A., Yang, Y., Luo, X., Yang, Y., Hu, L., Dai, Q., Dai, X., Chen, D., Luo, C., et al · 2024
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Contrastive learning: an efficient domain adaptation strategy for 2d mammography image classification
Quintana, G. I., Jugnon, V., Vancamberg, L., Desolneux, A., and Mougeot, M · 2024
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Optimizing clip models for image retrieval with maintained joint-embedding alignment
Schall, K., Barthel, K. U., Hezel, N., and Jung, K · 2024
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