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The Platonic Representation Hypothesis claims that recent foundation models are converging to a shared representation space as a function of their downstream task performance, irrespective of the objectives and data modalities used to train these models (Huh et al., 2024).
A large-scale study of representation learning with the visual task adaptation benchmark
Zhai, X., Puigcerver, J., Kolesnikov, A., Ruyssen, P., Riquelme, C., Lucic, M., Djolonga, J., Pinto, A. S., Neumann, M., Dosovitskiy, A., Beyer, L., Bachem, O., Tschannen, M., Michalski, M., Bousquet, O., Gelly, S., and Houlsby, N · 1910
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Relations Between Two Sets of Variates , pp. 162–190
Hotelling, H · 1992
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Improved baselines with momentum contrastive learning
Chen, X., Fan, H., Girshick, R., and He, K · 2003
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Measuring statistical dependence with hilbert-schmidt norms
Gretton, A., Bousquet, O., Smola, A., and Schölkopf, B · 2005
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Representational similarity analysis-connecting the branches of systems neuroscience
Kriegeskorte, N., Mur, M., and Bandettini, P. A · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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About WordNet
Princeton University · 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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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Convergent learning: Do different neural networks learn the same representations?
Li, Y., Yosinski, J., Clune, J., Lipson, H., and Hopcroft, J · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, 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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Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P · 2016
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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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SGDR: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2017
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Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
Raghu, M., Gilmer, J., Yosinski, J., and Sohl-Dickstein, J · 2017
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Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A · 2017
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Identification of drug-side effect association via multiple information integration with centered kernel alignment
Ding, Y., Tang, J., and Guo, F · 2018
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Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
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Squeeze-and-excitation networks
Hu, J., Shen, L., and Sun, G · 2018
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Deep neural networks as gaussian processes
Lee, J., Sohl-dickstein, J., Pennington, J., Novak, R., Schoenholz, S., and Bahri, Y · 2018
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Gaussian process behaviour in wide deep neural networks
Matthews, A., Hron, J., Rowland, M., Turner, R. E., and Ghahramani, Z · 2018
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Insights on representational similarity in neural networks with canonical correlation
Morcos, A., Raghu, M., and Bengio, S · 2018
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Representation similarity analysis for efficient task taxonomy & transfer learning
Dwivedi, K. and Roig, G · 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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Do better imagenet models transfer better?
Kornblith, S., Shlens, J., and Le, Q. V · 2019
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
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EfficientNet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A · 2020
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Big self-supervised models are strong semi-supervised learners
Chen, T., Kornblith, S., Swersky, K., Norouzi, M., and Hinton, G. E · 2020
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Beyond accuracy: quantifying trial-by-trial behaviour of cnns and humans by measuring error consistency
Geirhos, R., Meding, K., and Wichmann, F. A · 2020
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Let’s agree to agree: Neural networks share classification order on real datasets
Hacohen, G., Choshen, L., and Weinshall, D · 2020
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What shapes feature representations? exploring datasets, architectures, and training
Hermann, K. and Lampinen, A · 2020
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Self-supervised learning of pretext-invariant representations
Misra, I. and van der Maaten, L · 2020
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Contrastive representation distillation
Tian, Y., Krishnan, D., and Isola, P · 2020
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Revisiting model stitching to compare neural representations
Bansal, Y., Nakkiran, P., and Barak, B · 2021
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Implicit regularization via neural feature alignment
Baratin, A., George, T., Laurent, C., Devon Hjelm, R., Lajoie, G., Vincent, P., and Lacoste-Julien, S · 2021
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Emerging properties in self-supervised vision transformers
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A · 2021
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Similarity and matching of neural network representations
Csiszárik, A., Kőrösi-Szabó, P., Matszangosz, A., Papp, G., and Varga, D · 2021
Cited alongside, same era.
A convnet for the 2020s
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S · 2022
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Distilling representational similarity using centered kernel alignment (cka)
Saha, A., Bialkowski, A. N., and Khalifa, S · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., Schramowski, P., Kundurthy, S., Crowson, K., Schmidt, L., Kaczmarczyk, R., and Jitsev, J · 2022
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How to train your vit? data, augmentation, and regularization in vision transformers
Steiner, A. P., Kolesnikov, A., Zhai, X., Wightman, R., Uszkoreit, J., and Beyer, L · 2022
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Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
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Grounding representation similarity through statistical testing
Ding, F., Denain, J.-S., and Steinhardt, J · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Partial success in closing the gap between human and machine vision
Geirhos, R., Narayanappa, K., Mitzkus, B., Thieringer, T., Bethge, M., Wichmann, F. A., and Brendel, W · 2021
Cited alongside, same era.
Why do better loss functions lead to less transferable features?
Kornblith, S., Chen, T., Lee, H., and Norouzi, M · 2021
Cited alongside, same era.
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
Cited alongside, same era.
THINGSvision: A python toolbox for streamlining the extraction of activations from deep neural networks
Muttenthaler, L. and Hebart, M. N · 2021
Cited alongside, same era.
Distilling from similar tasks for transfer learning on a budget
Borup, K., Phoo, C. P., and Hariharan, B · 2023
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PaLI: A jointly-scaled multilingual language-image model
Chen, X., Wang, X., Changpinyo, S., Piergiovanni, A., Padlewski, P., Salz, D., Goodman, S., Grycner, A., Mustafa, B., Beyer, L., Kolesnikov, A., Puigcerver, J., Ding, N., Rong, K., Akbari, H., Mishra, G., Xue, L., Thapliyal, A. V., Bradbury, J., Kuo, W., Seyedhosseini, M., Jia, C., Ayan, B. K., Ruiz, C. R., Steiner, A. P., Angelova, A., Zhai, X., Houlsby, N., and Soricut, R · 2023
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Scaling vision transformers to 22 billion parameters
Dehghani, M., Djolonga, J., Mustafa, B., Padlewski, P., Heek, J., Gilmer, J., Steiner, A. P., Caron, M., Geirhos, R., Alabdulmohsin, I., Jenatton, R., Beyer, L., Tschannen, M., Arnab, A., Wang, X., Riquelme Ruiz, C., Minderer, M., Puigcerver, J., Evci, U., Kumar, M., Steenkiste, S. V., Elsayed, G. F., Mahendran, A., Yu, F., Oliver, A., Huot, F., Bastings, J., Collier, M., Gritsenko, A. A., Birodkar, V., Vasconcelos, C. N., Tay, Y., Mensink, T., Kolesnikov, A., Pavetic, F., Tran, D., Kipf, T., Lucic, M., Zhai, X., Keysers, D., Harmsen, J. J., and Houlsby, N · 2023
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Representational dissimilarity metric spaces for stochastic neural networks
Duong, L., Zhou, J., Nassar, J., Berman, J., Olieslagers, J., and Williams, A. H · 2023
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Towards measuring representational similarity of large language models
Klabunde, M., Amor, M. B., Granitzer, M., and Lemmerich, F · 2023
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Human-like systematic generalization through a meta-learning neural network
Lake, B. M. and Baroni, M · 2023
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Linearly mapping from image to text space
Merullo, J., Castricato, L., Eickhoff, C., and Pavlick, E · 2023
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Human alignment of neural network representations
Muttenthaler, L., Dippel, J., Linhardt, L., Vandermeulen, R. A., and Kornblith, S · 2023
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Getting aligned on representational alignment, 2023
Sucholutsky, I., Muttenthaler, L., Weller, A., Peng, A., Bobu, A., Kim, B., Love, B. C., Grant, E., Groen, I., Achterberg, J., Tenenbaum, J. B., Collins, K. M., Hermann, K. L., Oktar, K., Greff, K., Hebart, M. N., Jacoby, N., Zhang, Q., Marjieh, R., Geirhos, R., Chen, S., Kornblith, S., Rane, S., Konkle, T., O’Connell, T. P., Unterthiner, T., Lampinen, A. K., Müller, K.-R., Toneva, M., and Griffiths, T. L · 2023
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Eva-clip: Improved training techniques for clip at scale
Sun, Q., Fang, Y., Wu, L., Wang, X., and Cao, Y · 2023
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Gemini: a family of highly capable multimodal models
Team, G., Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., et al · 2023
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Sigmoid loss for language image pre-training
Zhai, X., Mustafa, B., Kolesnikov, A., and Beyer, L · 2023
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Better teacher better student: Dynamic prior knowledge for knowledge distillation
Zong, M., Qiu, Z., Ma, X., Yang, K., Liu, C., Hou, J., Yi, S., and Ouyang, W · 2023
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Wild comparisons: A study of how representation similarity changes when input data is drawn from a shifted distribution
Brown, D., Shapiro, M. R., Bittner, A., Warley, J., and Kvinge, H · 2024
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Position: The platonic representation hypothesis
Huh, M., Cheung, B., Wang, T., and Isola, P · 2024
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Align-base model, 2023
KakaoBrain · 2024
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Learned feature representations are biased by complexity, learning order, position, and more
Lampinen, A. K., Chan, S. C., and Hermann, K · 2024
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Do vision and language encoders represent the world similarly?
Maniparambil, M., Akshulakov, R., Dahou Djilali, Y. A., Seddik, M. E. A., Narayan, S., Mangalam, K., and O’Connor, N. E · 2024
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Aligning machine and human visual representations across abstraction levels
Muttenthaler, L., Greff, K., Born, F., Spitzer, B., Kornblith, S., Mozer, M. C., Müller, K.-R., Unterthiner, T., and Lampinen, A. K · 2024
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DINOv2: Learning robust visual features without supervision
Oquab, M., Darcet, T., Moutakanni, T., Vo, H. V., Szafraniec, M., Khalidov, V., Fernandez, P., HAZIZA, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P.-Y., Li, S.-W., Misra, I., Rabbat, M., Sharma, V., Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., and Bojanowski, P · 2024
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Ai can help humans find common ground in democratic deliberation
Tessler, M. H., Bakker, M. A., Jarrett, D., Sheahan, H., Chadwick, M. J., Koster, R., Evans, G., Campbell-Gillingham, L., Collins, T., Parkes, D. C., Botvinick, M., and Summerfield, C · 2024
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Learning vision from models rivals learning vision from data
Tian, Y., Fan, L., Chen, K., Katabi, D., Krishnan, D., and Isola, P · 2024
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Similarity of neural network models: A survey of functional and representational measures
Klabunde, M., Schumacher, T., Strohmaier, M., and Lemmerich, F · 2025
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A large-scale examination of inductive biases shaping high-level visual representation in brains and machines
Conwell, C., Prince, J. S., Kay, K. N., Alvarez, G. A., and Konkle, T · 2041
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Unmasking clever hans predictors and assessing what machines really learn
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Individual differences among deep neural network models
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Limits to visual representational correspondence between convolutional neural networks and the human brain
Xu, Y. and Vaziri-Pashkam, M · 2041
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