No Token Left Behind: Explainability-Aided Image Classification and Generation

Otsu, N.: A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics 9

1979

Earlier work this paper cites.

Berg, T., Forsyth, D.: Animals on the web. In: CVPR (2006)

2006

Earlier work this paper cites.

Vijayanarasimhan, S., Grauman, K.: Keywords to visual categories: Multiple-instance learning forweakly supervised object categorization. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1–8 (2008). https://doi.org/10.1109/CVPR.2008.4587632

2008

Earlier work this paper cites.

Wang, X.J., Zhang, L., Li, X., Ma, W.Y.: Annotating images by mining image search results. IEEE Transactions on Pattern Analysis and Machine Intelligence 30

2008

Earlier work this paper cites.

Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition pp. 248–255 (2009)

2009

Earlier work this paper cites.

Fergus, R., Fei-Fei, L., Perona, P., Zisserman, A.: Learning object categories from internet image searches. Proceedings of the IEEE 98

2010

Earlier work this paper cites.

Rubinstein, M., Joulin, A., Kopf, J., Liu, C.: Unsupervised joint object discovery and segmentation in internet images. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1939–1946 (2013). https://doi.org/10.1109/CVPR.2013.253

2013

Earlier work this paper cites.

Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision. pp. 740–755. Springer (2014)

2014

Earlier work this paper cites.

Tang, K., Joulin, A., Li, L.J., Fei-Fei, L.: Co-localization in real-world images. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1464–1471 (2014). https://doi.org/10.1109/CVPR.2014.190

2014

Earlier work this paper cites.

Chen, X., Gupta, A.K.: Webly supervised learning of convolutional networks. 2015 IEEE International Conference on Computer Vision (ICCV) pp. 1431–1439 (2015)

2015

Earlier work this paper cites.

Binder, A., Montavon, G., Lapuschkin, S., Müller, K.R., Samek, W.: Layer-wise relevance propagation for neural networks with local renormalization layers. In: International Conference on Artificial Neural Networks. pp. 63–71. Springer (2016)

2016

Earlier work this paper cites.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778 (2016)

2016

Earlier work this paper cites.

Shrikumar, A., Greenside, P., Shcherbina, A., Kundaje, A.: Not just a black box: Learning important features through propagating activation differences. arXiv preprint arXiv:1605.01713 (2016)

Original

2016

Earlier work this paper cites.

Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 30. Curran Associates, Inc. (2017), https://proceedings.neurips.cc/paper/2017/file/8a1d694707eb0fefe65871369074926d-Paper.pdf

2017

Earlier work this paper cites.

Li, A., Jabri, A., Joulin, A., van der Maaten, L.: Learning visual n-grams from web data. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (Oct 2017)

2017

Earlier work this paper cites.

Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems. pp. 4765–4774 (2017)

2017

Earlier work this paper cites.

Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision. pp. 618–626 (2017)

2017

Cited alongside, same era.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)

2017

Cited alongside, same era.

Akbik, A., Blythe, D., Vollgraf, R.: Contextual string embeddings for sequence labeling. In: COLING 2018, 27th International Conference on Computational Linguistics. pp. 1638–1649 (2018)

2018

Cited alongside, same era.

Akbik, A., Bergmann, T., Blythe, D., Rasul, K., Schweter, S., Vollgraf, R.: Flair: An easy-to-use framework for state-of-the-art nlp. In: NAACL 2019, 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations). pp. 54–59 (2019)

Chefer, H., Gur, S., Wolf, L.: Transformer interpretability beyond attention visualization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 782–791 (June 2021)

2021

Later among the works it cites.

Crowson, K.: Vqgan+clip. https://colab.research.google.com/drive/1L8oL-vLJXVcRzCFbPwOoMkPKJ8-aYdPN (2021)

2021

Later among the works it cites.

Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 12873–12883 (June 2021)

2021

Later among the works it cites.

Gal, R., Patashnik, O., Maron, H., Chechik, G., Cohen-Or, D.: Stylegan-nada: Clip-guided domain adaptation of image generators (2021)

2021

Later among the works it cites.