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Probes are small networks that predict properties of underlying data from embeddings, and they provide a targeted, effective way to illuminate the information contained in embeddings.
An optimum method for two-level rendition of continuous tone pictures
Bayer, B. E · 1973
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An adaptive algorithm for spatial gray-scale
Floyd, R. W. and Steinberg, L · 1976
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The OpenCV Library
Bradski, G · 2000
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Understanding image representations by measuring their equivariance and equivalence
Lenc, K. and Vedaldi, A · 2015
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Understanding deep image representations by inverting them
Mahendran, A. and Vedaldi, A · 2015
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Inceptionism: Going deeper into neural networks, 2015
Mordvintsev, A., Olah, C., and Tyka, M · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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Amplitude modulated line-based halftoning
Ahmed, A. G. and Deussen, O · 2016
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V · 2016
Earlier work this paper cites.
Perceptual losses for real-time style transfer and super-resolution
Johnson, J., Alahi, A., and Fei-Fei, L · 2016
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Understanding intermediate layers using linear classifier probes
Alain, G. and Bengio, Y · 2017
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Exploring the structure of a real-time, arbitrary neural artistic stylization network
Ghiasi, G., Lee, H., Kudlur, M., Dumoulin, V., and Shlens, J · 2017
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Google’s cloud vision api is not robust to noise
Hosseini, H., Xiao, B., and Poovendran, R · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Sun, C., Shrivastava, A., Singh, S., and Gupta, A · 2017
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What you can cram into a single &!#* vector: Probing sentence embeddings for linguistic properties
Conneau, A., Kruszewski, G., Lample, G., Barrault, L., and Baroni, M · 2018
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Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Hendrycks, D. and Dietterich, T · 2018
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Unsupervised representation learning by predicting image rotations
Komodakis, N. and Gidaris, S · 2018
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Goodman, D. and Wei, T · 2019
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Designing and interpreting probes with control tasks
Hewitt, J. and Liang, P · 2019
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A structural probe for finding syntax in word representations
Hewitt, J. and Manning, C. D · 2019
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Adversarial examples versus cloud-based detectors: A black-box empirical study
Li, X., Ji, S., Han, M., Ji, J., Ren, Z., Liu, Y., and Wu, C · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Stealthy porn: Understanding real-world adversarial images for illicit online promotion
Yuan, K., Tang, D., Liao, X., Wang, X., Feng, X., Chen, Y., Sun, M., Lu, H., and Zhang, K · 2019
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Cited alongside, same era.
Visual chirality
Lin, Z., Sun, J., Davis, A., and Snavely, N · 2020
Cited alongside, same era.
Evaluating self-supervised pretraining without using labels
Metzger, S., Srinivas, A., Darrell, T., and Keutzer, K · 2020
Cited alongside, same era.
What makes for good views for contrastive learning?
Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., and Isola, P · 2020
Cited alongside, same era.
What Should Not Be Contrastive in Contrastive Learning
Xiao, T., Wang, X., Efros, A. A., and Darrell, T · 2020
Cited alongside, same era.
Probing classifiers: Promises, shortcomings, and alternatives
Emergent world representations: Exploring a sequence model trained on a synthetic task
Li, K., Hopkins, A. K., Bau, D., Viégas, F., Pfister, H., and Wattenberg, M · 2022
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Things not written in text: Exploring spatial commonsense from visual signals
Liu, X., Yin, D., Feng, Y., and Zhao, D · 2022
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López, J. A. H., Weyssow, M., Cuadrado, J. S., and Sahraoui, H · 2022
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Enhance the visual representation via discrete adversarial training
Mao, X., Chen, Y., Duan, R., Zhu, Y., Qi, G., Ye, S., Li, X., Zhang, R., and Xue, H · 2022
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Belinkov, Y · 2021
Cited alongside, same era.
On the dangers of stochastic parrots: Can language models be too big?
Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S · 2021
Cited alongside, same era.
On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., and Brunskill, E · 2021
Cited alongside, same era.
Equivariant self-supervised learning: Encouraging equivariance in representations
Dangovski, R., Jing, L., Loh, C., Han, S., Srivastava, A., Cheung, B., Agrawal, P., and Soljacic, M · 2021
Cited alongside, same era.
It’s not what it looks like: Manipulating perceptual hashing based applications
Hao, Q., Luo, L., Jan, S. T., and Wang, G · 2021
Cited alongside, same era.
Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2021
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision
Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q., Sung, Y.-H., Li, Z., and Duerig, T · 2021
Cited alongside, same era.
Mishra, S., Robinson, J., Chang, H., Jacobs, D., Sarna, A., Maschinot, A., and Krishnan, D · 2022
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Quality not quantity: On the interaction between dataset design and robustness of clip
Nguyen, T., Ilharco, G., Wortsman, M., Oh, S., and Schmidt, L · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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Simplified transfer learning for chest radiography models using less data
Sellergren, A. B., Chen, C., Nabulsi, Z., Li, Y., Maschinot, A., Sarna, A., Huang, J., Lau, C., Kalidindi, S. R., and Etemadi, M · 2022
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Connect, not collapse: Explaining contrastive learning for unsupervised domain adaptation
Shen, K., Jones, R. M., Kumar, A., Xie, S. M., HaoChen, J. Z., Ma, T., and Liang, P · 2022
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Beyond invariance: Test-time label-shift adaptation for distributions with “spurious" correlations
Sun, Q., Murphy, K., Ebrahimi, S., and D’Amour, A · 2022
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Probing pretrained models of source code
Troshin, S. and Chirkova, N · 2022
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python-pillow/pillow: 9.2.0, July 2022
van Kemenade, H., Murray, A., wiredfool, Jeffrey A. Clark, A., Karpinsky, A., Baranovič, O., Gohlke, C., Dufresne, J., DWesl, Schmidt, D., Kopachev, K., Houghton, A., Mani, S., Landey, S., vashek, Ware, J., Piolie, Douglas, J., T., S., Caro, D., Martinez, U., Kossouho, S., Lahd, R., Lee, A., Brown, E. W., Tonnhofer, O., Bonfill, M., and Base, M · 2022
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Diffusion models: A comprehensive survey of methods and applications
Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Shao, Y., Zhang, W., Cui, B., and Yang, M.-H · 2022
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Visual commonsense in pretrained unimodal and multimodal models
Zhang, C., Van Durme, B., Li, Z., and Stengel-Eskin, E · 2022
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Ood-probe: A neural interpretation of out-of-domain generalization
Zhu, Z., Shahtalebi, S., and Rudzicz, F · 2022
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Probing graph representations
Akhondzadeh, M. S., Lingam, V., and Bojchevski, A · 2023
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Eliciting latent predictions from transformers with the tuned lens
Belrose, N., Furman, Z., Smith, L., Halawi, D., Ostrovsky, I., McKinney, L., Biderman, S., and Steinhardt, J · 2023
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Muse: Text-to-image generation via masked generative transformers
Chang, H., Zhang, H., Barber, J., Maschinot, A., Lezama, J., Jiang, L., Yang, M.-H., Murphy, K., Freeman, W. T., Rubinstein, M., et al · 2023
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Simple disentanglement of style and content in visual representations
Ngweta, L., Maity, S., Gittens, A., Sun, Y., and Yurochkin, M · 2023
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Benchmarking robustness to adversarial image obfuscations
Stimberg, F., Chakrabarti, A., Lu, C.-T., Hazimeh, H., Stretcu, O., Qiao, W., Liu, Y., Kaya, M., Rashtchian, C., Fuxman, A., et al · 2023
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Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation
Wei, Y., Zhang, Y., Ji, Z., Bai, J., Zhang, L., and Zuo, W · 2023
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A comprehensive survey on pretrained foundation models: A history from bert to chatgpt
Zhou, C., Li, Q., Li, C., Yu, J., Liu, Y., Wang, G., Zhang, K., Ji, C., Yan, Q., He, L., et al · 2023
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