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Deep neural networks have achieved success across a wide range of applications, including as models of human behavior and neural representations in vision tasks.
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An image is worth 16x16 words: Transformers for image recognition at scale
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A brief review of domain adaptation
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Scaling up visual and vision-language representation learning with noisy text supervision
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An ecologically motivated image dataset for deep learning yields better models of human vision
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Revisiting the calibration of modern neural networks
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Places: A 10 million image database for scene recognition
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Generalisation in humans and deep neural networks
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Evaluating (and improving) the correspondence between deep neural networks and human representations
J. C. Peterson, J. T. Abbott, and T. L. Griffiths · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
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Thingsvision: A python toolbox for streamlining the extraction of activations from deep neural networks
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Learning transferable visual models from natural language supervision
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Deep problems with neural network models of human vision
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What can 5.17 billion regression fits tell us about the representational format of the high-level human visual system?
T. Konkle, C. Conwell, J. S. Prince, and G. A. Alvarez · 2022
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VICE: Variational Interpretable Concept Embeddings
L. Muttenthaler, C. Y. Zheng, P. McClure, R. A. Vandermeulen, M. N. Hebart, and F. Pereira · 2022
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Challenges in deploying machine learning: a survey of case studies
A. Paleyes, R.-G. Urma, and N. D. Lawrence · 2022
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Winoground: Probing vision and language models for visio-linguistic compositionality
T. Thrush, R. Jiang, M. Bartolo, A. Singh, A. Williams, D. Kiela, and C. Ross · 2022
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Tip-adapter: Training-free adaption of clip for few-shot classification
R. Zhang, W. Zhang, R. Fang, P. Gao, K. Li, J. Dai, Y. Qiao, and H. Li · 2022
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Getting vit in shape: Scaling laws for compute-optimal model design
I. M. Alabdulmohsin, X. Zhai, A. Kolesnikov, and L. Beyer · 2023
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Pali-3 vision language models: Smaller, faster, stronger
X. Chen, X. Wang, L. Beyer, A. Kolesnikov, J. Wu, P. Voigtlaender, B. Mustafa, S. Goodman, I. M. Alabdulmohsin, P. Padlewski, D. M. Salz, X. Xiong, D. Vlasic, F. Pavetic, K. Rong, T. Yu, D. Keysers, X.-Q. Zhai, and R. Soricut · 2023
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Relational reasoning and generalization using nonsymbolic neural networks
A. Geiger, A. Carstensen, M. C. Frank, and C. Potts · 2023
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A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al · 2023
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Getting aligned on representational alignment
I. Sucholutsky, L. Muttenthaler, A. Weller, A. Peng, A. Bobu, B. Kim, B. C. Love, E. Grant, J. Achterberg, J. B. Tenenbaum, K. M. Collins, K. L. Hermann, K. Oktar, K. Greff, M. Hebart, N. Jacoby, Q. Zhang, R. Marjieh, R. Geirhos, S. Chen, S. Kornblith, S. Rane, T. Konkle, T. P. O’Connell, T. Unterthiner, A. K. Lampinen, K.-R. Müller, M. Toneva, and T. L. Griffiths · 2023
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Gemini: a family of highly capable multimodal models
G. Team, R. Anil, S. Borgeaud, Y. Wu, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, and A. Hauth · 2023
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Image captioners are scalable vision learners too
M. Tschannen, M. Kumar, A. Steiner, X. Zhai, N. Houlsby, and L. Beyer · 2023
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PaliGemma: A versatile 3B VLM for transfer
L. Beyer, A. Steiner, A. S. Pinto, A. Kolesnikov, X. Wang, D. Salz, M. Neumann, I. Alabdulmohsin, M. Tschannen, E. Bugliarello, T. Unterthiner, D. Keysers, S. Koppula, F. Liu, A. Grycner, A. Gritsenko, N. Houlsby, M. Kumar, K. Rong, J. Eisenschlos, R. Kabra, M. Bauer, M. Bošnjak, X. Chen, M. Minderer, P. Voigtlaender, I. Bica, I. Balazevic, J. Puigcerver, P. Papalampidi, O. Henaff, X. Xiong, R. Soricut, J. Harmsen, and X. Zhai · 2024
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A comprehensive study on robustness of image classification models: Benchmarking and rethinking
C. Liu, Y. Dong, W. Xiang, X. Yang, H. Su, J. Zhu, Y. Chen, Y. He, H. Xue, and S. Zheng · 2024
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Set learning for accurate and calibrated models
L. Muttenthaler, R. A. Vandermeulen, Q. Zhang, T. Unterthiner, and K. R. Muller · 2024
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DINOv2: Learning robust visual features without supervision
M. Oquab, T. Darcet, T. Moutakanni, H. V. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. HAZIZA, F. Massa, A. El-Nouby, M. Assran, N. Ballas, W. Galuba, R. Howes, P.-Y. Huang, S.-W. Li, I. Misra, M. Rabbat, V. Sharma, G. Synnaeve, H. Xu, H. Jegou, J. Mairal, P. Labatut, A. Joulin, and P. Bojanowski · 2024
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Gemini Team, G. Comanici, E. Bieber, M. Schaekermann, I. Pasupat, N. Sachdeva, I. Dhillon, M. Blistein, O. Ram, D. Zhang, E. Rosen, et al · 2025
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SigLIP 2: Multilingual vision-language encoders with improved semantic understanding, localization, and dense features
M. Tschannen, A. Gritsenko, X. Wang, M. F. Naeem, I. Alabdulmohsin, N. Parthasarathy, T. Evans, L. Beyer, Y. Xia, B. Mustafa, O. Hénaff, J. Harmsen, A. Steiner, and X. Zhai · 2025
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Things-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior
M. N. Hebart, O. Contier, L. Teichmann, A. H. Rockter, C. Y. Zheng, A. Kidder, A. Corriveau, M. Vaziri-Pashkam, and C. I. Baker · 2050
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