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Vision Transformers (ViTs) have achieved state-of-the-art performance for various vision tasks.
Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
Earlier work this paper cites.
Imagenet auto-annotation with segmentation propagation
Guillaumin, M., Küttel, D., and Ferrari, V · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W · 2015
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Show, attend and tell: Neural image caption generation with visual attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., and Bengio, Y · 2015
Earlier work this paper cites.
Layer-wise relevance propagation for neural networks with local renormalization layers
Binder, A., Montavon, G., Lapuschkin, S., Müller, K.-R., and Samek, W · 2016
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The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B · 2016
Earlier work this paper cites.
” why should i trust you?” explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Between pure and approximate differential privacy
Steinke, T. and Ullman, J · 2016
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End-to-end training of object class detectors for mean average precision
Henderson, P. and Ferrari, V · 2017
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The promise and peril of human evaluation for model interpretability
Herman, B · 2017
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A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
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Rényi differential privacy
Mironov, I · 2017
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Explaining nonlinear classification decisions with deep taylor decomposition
Montavon, G., Lapuschkin, S., Binder, A., Samek, W., and Müller, K.-R · 2017
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Right for the right reasons: Training differentiable models by constraining their explanations
Ross, A. S., Hughes, M. C., and Doshi-Velez, F · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
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Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B · 2018
Cited alongside, same era.
L-shapley and c-shapley: Efficient model interpretation for structured data
Chen, J., Song, L., Wainwright, M. J., and Jordan, M. I · 2018
Cited alongside, same era.
Tell me where to look: Guided attention inference network
Li, K., Wu, Z., Peng, K.-C., Ernst, J., and Fu, Y · 2018
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Cohen, J., Rosenfeld, E., and Kolter, Z · 2019
Cited alongside, same era.
Explanations can be manipulated and geometry is to blame
Dombrowski, A.-K., Alber, M., Anders, C., Ackermann, M., Müller, K.-R., and Kessel, P · 2019
Cited alongside, same era.
Techniques for interpretable machine learning
Du, M., Liu, N., and Hu, X · 2019
Transformer interpretability beyond attention visualization
Chefer, H., Gur, S., and Wolf, L · 2021
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Pre-trained image processing transformer
Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., and Gao, W · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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Fnet: Mixing tokens with fourier transforms
Lee-Thorp, J., Ainslie, J., Eckstein, I., and Ontanon, S · 2021
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On the robustness of vision transformers to adversarial examples
Mahmood, K., Mahmood, R., and Van Dijk, M · 2021
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Cited alongside, same era.
Interpretation of neural networks is fragile
Ghorbani, A., Abid, A., and Zou, J · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Kenton, J. D. M.-W. C. and Toutanova, L. K · 2019
Cited alongside, same era.
The (un) reliability of saliency methods
Kindermans, P.-J., Hooker, S., Adebayo, J., Alber, M., Schütt, K. T., Dähne, S., Erhan, D., and Kim, B · 2019
Cited alongside, same era.
Certified adversarial robustness with additive noise
Li, B., Chen, C., Wang, W., and Carin, L · 2019
Cited alongside, same era.
Interpretable spatio-temporal attention for video action recognition
Meng, L., Zhao, B., Chang, B., Huang, G., Sun, W., Tung, F., and Sigal, L · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
Cited alongside, same era.
Naseer, M. M., Ranasinghe, K., Khan, S. H., Hayat, M., Shahbaz Khan, F., and Yang, M.-H · 2021
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Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P · 2021
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Training data-efficient image transformers & distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and Jégou, H · 2021
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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P. H., et al · 2021
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Deformable transformers for end-to-end object detection
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai J F, D. D · 2021
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Seat: Stable and explainable attention
Hu, L., Liu, Y., Liu, N., Huai, M., Sun, L., and Wang, D · 2022
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Towards faithful model explanation in nlp: A survey
Lyu, Q., Apidianaki, M., and Callison-Burch, C · 2022
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Towards robust vision transformer
Mao, X., Qi, G., Chen, Y., Li, X., Duan, R., Ye, S., He, Y., and Xue, H · 2022
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Diffusion models for adversarial purification
Nie, W., Guo, B., Huang, Y., Xiao, C., Vahdat, A., and Anandkumar, A · 2022
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Vision transformers are robust learners
Paul, S. and Chen, P.-Y · 2022
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Certified patch robustness via smoothed vision transformers
Salman, H., Jain, S., Wong, E., and Madry, A · 2022
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On the sensitivity and stability of model interpretations in nlp
Yin, F., Shi, Z., Hsieh, C.-J., and Chang, K.-W · 2022
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Understanding the robustness in vision transformers
Zhou, D., Yu, Z., Xie, E., Xiao, C., Anandkumar, A., Feng, J., and Alvarez, J. M · 2022
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(certified!!) adversarial robustness for free!
Carlini, N., Tramer, F., Dvijotham, K. D., Rice, L., Sun, M., and Kolter, J. Z · 2023
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