Fetching the paper…
Reading the bibliography…
Modern vision models typically rely on fine-tuning general-purpose models pre-trained on large, static datasets.
Dyna, an integrated architecture for learning, planning, and reacting
Sutton, R. S · 1991
Earlier work this paper cites.
Wordnet: a lexical database for english
Miller, G. A · 1995
Earlier work this paper cites.
Gaussian processes for regression
Williams, C. and Rasmussen, C · 1995
Earlier work this paper cites.
Learning object categories from google’s image search
Fergus, R., Fei-Fei, L., Perona, P., and Zisserman, A · 2005
Earlier work this paper cites.
Automated flower classification over a large number of classes
Nilsback, M.-E. and Zisserman, A · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Active learning literature survey
Settles, B · 2009
Earlier work this paper cites.
Toward an architecture for never-ending language learning
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E. R., and Mitchell, T. M · 2010
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A · 2010
Earlier work this paper cites.
Learning about canonical views from internet image collections
Mezuman, E. and Weiss, Y · 2012
Earlier work this paper cites.
Cats and dogs
Parkhi, O. M., Vedaldi, A., Zisserman, A., and Jawahar, C · 2012
Earlier work this paper cites.
Neil: Extracting visual knowledge from web data
Chen, X., Shrivastava, A., and Gupta, A · 2013
Earlier work this paper cites.
Birdsnap: Large-scale fine-grained visual categorization of birds
Berg, T., Liu, J., Woo Lee, S., Alexander, M. L., Jacobs, D. W., and Belhumeur, P. N · 2014
Earlier work this paper cites.
Food-101–mining discriminative components with random forests
Bossard, L., Guillaumin, M., and Gool, L. V · 2014
Earlier work this paper cites.
Webly supervised learning of convolutional networks
Chen, X. and Gupta, A · 2015
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., and Philbin, J · 2015
Earlier work this paper cites.
Yfcc100m: The new data in multimedia research
Thomee, B., Shamma, D. A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D., and Li, L.-J · 2015
Earlier work this paper cites.
Google images download
Vasa, H · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Deep metric learning via lifted structured feature embedding
Oh Song, H., Xiang, Y., Jegelka, S., and Savarese, S · 2016
Cited alongside, same era.
Smart mining for deep metric learning
Harwood, B., Kumar BG, V., Carneiro, G., Reid, I., and Drummond, T · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions
Koh, P. W. and Liang, P · 2017
Cited alongside, same era.
Sampling matters in deep embedding learning
Wu, C.-Y., Manmatha, R., Smola, A. J., and Krahenbuhl, P · 2017
Cited alongside, same era.
Large batch training of convolutional networks
You, Y., Gitman, I., and Ginsburg, B · 2017
Cited alongside, same era.
Contrastive learning with hard negative samples
Robinson, J., Chuang, C.-Y., Sra, S., and Jegelka, S · 2020
Later among the works it cites.
Contrastive multiview coding
Tian, Y., Krishnan, D., and Isola, P · 2020
Later among the works it cites.
Beit: Bert pre-training of image transformers
Bao, H., Dong, L., and Wei, F · 2021
Later among the works it cites.
Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Bardes, A., Ponce, J., and LeCun, Y · 2021
Later among the works it cites.
imagehash (fork)
Buchner, J · 2021
Later among the works it cites.
Emerging properties in self-supervised vision transformers
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Functional map of the world
Christie, G., Fendley, N., Wilson, J., and Mukherjee, R · 2018
Cited alongside, same era.
Deep metric learning with hierarchical triplet loss
Ge, W · 2018
Cited alongside, same era.
Exploring the limits of weakly supervised pretraining
Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., and van der Maaten, L · 2018
Cited alongside, same era.
Never-ending learning
Mitchell, T., Cohen, W., Hruschka, E., Talukdar, P., Yang, B., Betteridge, J., Carlson, A., Dalvi, B., Gardner, M., Kisiel, B., et al · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
Cited alongside, same era.
Billion-scale similarity search with GPUs
Johnson, J., Douze, M., and Jégou, H · 2019
Cited alongside, same era.
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A · 2021
Later among the works it cites.
An empirical study of training self-supervised vision transformers
Chen, X., Xie, S., and He, K · 2021
Later among the works it cites.
Improving contrastive learning on imbalanced data via open-world sampling
Jiang, Z., Chen, T., Chen, T., and Wang, Z · 2021
Later among the works it cites.
Deep learning on a data diet: Finding important examples early in training
Paul, M., Ganguli, S., and Dziugaite, G. K · 2021
Later among the works it cites.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Later among the works it cites.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Wang, B. and Komatsuzaki, A · 2021
Later among the works it cites.
Barlow twins: Self-supervised learning via redundancy reduction
Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S · 2021
Later among the works it cites.
Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
Later among the works it cites.
Datamodels: Predicting predictions from training data
Ilyas, A., Park, S. M., Engstrom, L., Leclerc, G., and Madry, A · 2022
Later among the works it cites.
Webly supervised concept expansion for general purpose vision models
Kamath, A., Clark, C., Gupta, T., Kolve, E., Hoiem, D., and Kembhavi, A · 2022
Later among the works it cites.
Understanding collapse in non-contrastive siamese representation learning
Li, A. C., Efros, A. A., and Pathak, D · 2022
Later among the works it cites.
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., et al · 2022
Later among the works it cites.
Dinov2: Learning robust visual features without supervision
Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., et al · 2023
Closest in time.