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Image classifiers are information-discarding machines, by design.
A tight analysis of the greedy algorithm for set cover
Slavík, P · 1996
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Algorithm Design
Kleinberg, J. and Tardos, E · 2005
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Torchvision the machine-vision package of torch
Marcel, S. and Rodriguez, Y · 2010
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Spatial transformer networks
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Fine-grained recognition without part annotations
Krause, J., Jin, H., Yang, J., and Fei-Fei, L · 2015
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Imagenet large scale visual recognition challenge
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Apac: Augmented pattern classification with neural networks
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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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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Nguyen, A., Yosinski, J., and Clune, J · 2016
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Zagoruyko, S. and Komodakis, N · 2016
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Object recognition with and without objects
Zhu, Z., Xie, L., and Yuille, A. L · 2016
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Network dissection: Quantifying interpretability of deep visual representations
Bau, D., Zhou, B., Khosla, A., Oliva, A., and Torralba, A · 2017
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Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition
Fu, J., Zheng, H., and Mei, T · 2017
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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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Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K · 2017
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Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks
Ayhan, M. S. and Berens, P · 2018
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ma, N., Zhang, X., Zheng, H.-T., and Sun, J · 2018
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Learning to zoom: a saliency-based sampling layer for neural networks
Recasens, A., Kellnhofer, P., Stent, S., Matusik, W., and Torralba, A · 2018
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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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Understanding measures of uncertainty for adversarial example detection
Smith, L. and Gal, Y · 2018
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Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects
Alcorn, M. A., Li, Q., Gong, Z., Wang, C., Mai, L., Ku, W.-S., and Nguyen, A · 2019
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Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Barbu, A., Mayo, D., Alverio, J., Luo, W., Wang, C., Gutfreund, D., Tenenbaum, J., and Katz, B · 2019
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Damagenet: A universal adversarial dataset
Chen, S., Huang, X., He, Z., and Sun, C · 2019
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Does object recognition work for everyone?
De Vries, T., Misra, I., Wang, C., and Van der Maaten, L · 2019
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Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2019
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Lvis: A dataset for large vocabulary instance segmentation
Gupta, A., Dollar, P., and Girshick, R · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Augmix: A simple data processing method to improve robustness and uncertainty
Hendrycks, D., Mu, N., Cubuk, E. D., Zoph, B., Gilmer, J., and Lakshminarayanan, B · 2019
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Searching for mobilenetv3
Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al · 2019
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Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B · 2021
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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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Do vision transformers see like convolutional neural networks?
Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., and Dosovitskiy, A · 2021
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Can contrastive learning avoid shortcut solutions?
Robinson, J., Sun, L., Yu, K., Batmanghelich, K., Jegelka, S., and Sra, S · 2021
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Better aggregation in test-time augmentation
Shanmugam, D., Blalock, D., Balakrishnan, G., and Guttag, J · 2021
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How to train your vit? data, augmentation, and regularization in vision transformers
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Mixup inference: Better exploiting mixup to defend adversarial attacks
Pang, T., Xu, K., and Zhu, J · 2019
Cited alongside, same era.
Do imagenet classifiers generalize to imagenet?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 2019
Cited alongside, same era.
Learning robust global representations by penalizing local predictive power
Wang, H., Ge, S., Lipton, Z., and Xing, E. P · 2019
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Looking for the devil in the details: Learning trilinear attention sampling network for fine-grained image recognition
Zheng, H., Fu, J., Zha, Z.-J., and Luo, J · 2019
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Classification confidence estimation with test-time data-augmentation
Bahat, Y. and Shakhnarovich, G · 2020
Cited alongside, same era.
Beyer, L., Hénaff, O. J., Kolesnikov, A., Zhai, X., and Oord, A. v. d · 2020
Cited alongside, same era.
Steiner, A., Kolesnikov, A., Zhai, X., Wightman, R., Uszkoreit, J., and Beyer, L · 2021
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Fine-grained image analysis with deep learning: A survey
Wei, X.-S., Song, Y.-Z., Mac Aodha, O., Wu, J., Peng, Y., Tang, J., Yang, J., and Belongie, S · 2021
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Resnet strikes back: An improved training procedure in timm
Wightman, R., Touvron, H., and Jégou, H · 2021
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Re-labeling imagenet: from single to multi-labels, from global to localized labels
Yun, S., Oh, S. J., Heo, B., Han, D., Choe, J., and Chun, S · 2021
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Memo: Test time robustness via adaptation and augmentation
Zhang, M., Levine, S., and Finn, C · 2021
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https://iclr.cc/
Iclr 2023 · 2022
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Optimizing relevance maps of vision transformers improves robustness
Chefer, H., Schwartz, I., and Wolf, L · 2022
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Cyclic test time augmentation with entropy weight method
Chun, S., Lee, J. Y., and Kim, J · 2022
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Deformable protopnet: An interpretable image classifier using deformable prototypes
Donnelly, J., Barnett, A. J., and Chen, C · 2022
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Efficient classification of very large images with tiny objects
Kong, F. and Henao, R · 2022
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A convnet for the 2020s
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S · 2022
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Bugs in the data: How imagenet misrepresents biodiversity
Luccioni, A. S. and Rolnick, D · 2022
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Simple open-vocabulary object detection with vision transformers
Minderer, M., Gritsenko, A., Stone, A., Neumann, M., Weissenborn, D., Dosovitskiy, A., Mahendran, A., Arnab, A., Dehghani, M., Shen, Z., et al · 2022
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Visual correspondence-based explanations improve ai robustness and human-ai team accuracy
Taesiri, M. R., Nguyen, G., and Nguyen, A · 2022
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Maxvit: Multi-axis vision transformer
Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., and Li, Y · 2022
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Regionclip: Region-based language-image pretraining
Zhong, Y., Yang, J., Zhang, P., Li, C., Codella, N., Li, L. H., Zhou, L., Dai, X., Yuan, L., Li, Y., et al · 2022
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https://huggingface.co/timm/tf_efficientnet_l2.ns_jft_in1k_475
timm/tf_efficientnet_l2.ns_jft_in1k_475 · hugging face · 2023
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https://pytorch.org/vision/main/generated/torchvision.transforms.CenterCrop.html
Centercrop — torchvision main documentation · 2023
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https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html
Randomresizedcrop — torchvision main documentation · 2023
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Scaling up gans for text-to-image synthesis
Kang, M., Zhu, J.-Y., Zhang, R., Park, J., Shechtman, E., Paris, S., and Park, T · 2023
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Chatgpt api
OpenAI · 2023
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Learning to zoom and unzoom
Thavamani, C., Li, M., Ferroni, F., and Ramanan, D · 2023
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