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Our paper addresses the problem of models struggling to learn diverse features, due to either forgetting previously learned features or failing to learn new ones.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and < < 0.5 mb model size
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., and Keutzer, K · 2016
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Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A. and Valpola, H · 2017
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Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
Zhang, X., Zhou, X., Lin, M., and Sun, J · 2018
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Exploration by random network distillation
Burda, Y., Edwards, H., Storkey, A., and Klimov, O · 2019
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Self-ensembling with gan-based data augmentation for domain adaptation in semantic segmentation
Choi, J., Kim, T., and Kim, C · 2019
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Be your own teacher: Improve the performance of convolutional neural networks via self distillation
Zhang, L., Song, J., Gao, A., Chen, J., Bao, C., and Ma, K · 2019
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Background data resampling for outlier-aware classification
Li, Y. and Vasconcelos, N · 2020
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The role of disentanglement in generalisation
Montero, M. L., Ludwig, C. J., Costa, R. P., Malhotra, G., and Bowers, J · 2020
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Cross-domain few-shot classification via learned feature-wise transformation
Tseng, H.-Y., Lee, H.-Y., Huang, J.-B., and Yang, M.-H · 2020
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Self-knowledge distillation with progressive refinement of targets
Kim, K., Ji, B., Yoon, D., and Hwang, S · 2021
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Projected gans converge faster
Sauer, A., Chitta, K., Müller, J., and Geiger, A · 2021
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Debiased visual question answering from feature and sample perspectives
Wen, Z., Xu, G., Tan, M., Wu, Q., and Wu, Q · 2021
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Understanding deep learning (still) requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2021
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Learning fast, learning slow: A general continual learning method based on complementary learning system
Arani, E., Sarfraz, F., and Zonooz, B · 2022
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Perception prioritized training of diffusion models
Choi, J., Lee, J., Shin, C., Kim, S., Kim, H., and Yoon, S · 2022
Improving sketch colorization using adversarial segmentation consistency
Hicsonmez, S., Samet, N., Akbas, E., and Duygulu, P · 2023
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Cafa: Class-aware feature alignment for test-time adaptation
Jung, S., Lee, J., Kim, N., Shaban, A., Boots, B., and Choo, J · 2023
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Last layer re-training is sufficient for robustness to spurious correlations
Kirichenko, P., Izmailov, P., and Wilson, A. G · 2023
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Plastic: Improving input and label plasticity for sample efficient reinforcement learning
Lee, H., Cho, H., Kim, H., Gwak, D., Kim, J., Choo, J., Yun, S.-Y., and Yun, C · 2023
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Learning diverse features in vision transformers for improved generalization, 2023
Nicolicioiu, A. M., Nicolicioiu, A. L., Alexe, B., and Teney, D · 2023
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Ensemble deep learning: A review
Ganaie, M. A., Hu, M., Malik, A., Tanveer, M., and Suganthan, P · 2022
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Continual pre-training of language models
Ke, Z., Shao, Y., Lin, H., Konishi, T., Kim, G., and Liu, B · 2022
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Learning debiased classifier with biased committee
Kim, N., Hwang, S., Ahn, S., Park, J., and Kwak, S · 2022
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Continual test-time domain adaptation
Wang, Q., Fink, O., Van Gool, L., and Dai, D · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al · 2022
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Yang, Y., Peng, Z., Du, X., Tao, Z., Tang, J., and Pan, J · 2022
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Deep reinforcement learning with plasticity injection
Nikishin, E., Oh, J., Ostrovski, G., Lyle, C., Pascanu, R., Dabney, W., and Barreto, A · 2023
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Training debiased subnetworks with contrastive weight pruning
Park, G. Y., Lee, S., Lee, S. W., and Ye, J. C · 2023
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Analyzing bias in diffusion-based face generation models
Perera, M. V. and Patel, V. M · 2023
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Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models
Schramowski, P., Brack, M., Deiseroth, B., and Kersting, K · 2023
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Deep active learning for computer vision: Past and future
Takezoe, R., Liu, X., Mao, S., Chen, M. T., Feng, Z., Zhang, S., Wang, X., et al · 2023
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Erm++: An improved baseline for domain generalization
Teterwak, P., Saito, K., Tsiligkaridis, T., Saenko, K., and Plummer, B. A · 2023
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Pytorch implementation of cifar-100 training
Weiaicunzai · 2023
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Drm: Mastering visual reinforcement learning through dormant ratio minimization
Xu, G., Zheng, R., Liang, Y., Wang, X., Yuan, Z., Ji, T., Luo, Y., Liu, X., Yuan, J., Hua, P., Li, S., Ze, Y., au2, H. D. I., Huang, F., and Xu, H · 2024
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