Fetching the paper…
Reading the bibliography…
Costly, noisy, and over-specialized, labels are to be set aside in favor of unsupervised learning if we hope to learn cheap, reliable, and transferable models.
On the use of the geodesic metric in image analysis
Lantuéjoul, C. and Beucher, S · 1981
Earlier work this paper cites.
Geodesic methods in quantitative image analysis
Lantuéjoul, C. and Maisonneuve, F · 1984
Earlier work this paper cites.
Mixture density networks
Bishop, C. M · 1994
Earlier work this paper cites.
Distance metrics on the rigid-body motions with applications to mechanism design
Park, F. C · 1995
Earlier work this paper cites.
A rotation invariant pattern signature
Simoncelli, E · 1996
Earlier work this paper cites.
Invariant error metrics for image reconstruction
Fienup, J. R · 1997
Earlier work this paper cites.
Nonlinear dimensionality reduction by locally linear embedding
Roweis, S. T. and Saul, L. K · 2000
Earlier work this paper cites.
Laplacian eigenmaps and spectral techniques for embedding and clustering
Belkin, M. and Niyogi, P · 2001
Earlier work this paper cites.
The isomap algorithm and topological stability
Balasubramanian, M. and Schwartz, E. L · 2002
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Hinton, G. E · 2002
Earlier work this paper cites.
Out-of-sample extensions for lle, isomap, mds, eigenmaps, and spectral clustering
Bengio, Y., Paiement, J.-f., Vincent, P., Delalleau, O., Roux, N., and Ouimet, M · 2003
Earlier work this paper cites.
A unifying theorem for spectral embedding and clustering
Brand, M. and Huang, K · 2003
Earlier work this paper cites.
Improved baselines with momentum contrastive learning
Chen, X., Fan, H., Girshick, R., and He, K · 2003
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P · 2004
Earlier work this paper cites.
Image manifolds which are isometric to euclidean space
Donoho, D. L. and Grimes, C · 2005
Earlier work this paper cites.
Bilinear sparse coding for invariant vision
Grimes, D. B. and Rao, R. P · 2005
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Hyvärinen, A. and Dayan, P · 2005
Earlier work this paper cites.
High-resolution navigation on non-differentiable image manifolds
Wakin, M. B., Donoho, D. L., Choi, H., and Baraniuk, R. G · 2005
Earlier work this paper cites.
Translation insensitive image similarity in complex wavelet domain
Wang, Z. and Simoncelli, E. P · 2005
Earlier work this paper cites.
Improving geodesic distance estimation based on locally linear assumption
Meng, D., Leung, Y., Xu, Z., Fung, T., and Zhang, Q · 2008
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.
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A · 2008
Earlier work this paper cites.
Overview of supervised learning
Hastie, T., Tibshirani, R., and Friedman, J · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A., and Bottou, L · 2010
Earlier work this paper cites.
Cub200 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
Earlier work this paper cites.
Deep learning of representations for unsupervised and transfer learning
Bengio, 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.
Generating sequences with recurrent neural networks
Graves, A · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
3d object representations for fine-grained categorization
Krause, J., Stark, M., Deng, J., and Fei-Fei, L · 2013
Earlier work this paper cites.
Fine-grained visual classification of aircraft
Maji, S., Kannala, J., Rahtu, E., Blaschko, M., and Vedaldi, A · 2013
Earlier work this paper cites.
Geodesic regression and the theory of least squares on riemannian manifolds
Thomas Fletcher, P · 2013
Earlier work this paper cites.
Food-101 – mining discriminative components with random forests
Bossard, L., Guillaumin, M., and Van Gool, L · 2014
Earlier work this paper cites.
Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., , and Vedaldi, A · 2014
Cited alongside, same era.
One weird trick for parallelizing convolutional neural networks
Krizhevsky, A · 2014
Cited alongside, same era.
Discriminative unsupervised feature learning with exemplar convolutional neural networks
Alexey, D., Fischer, P., Tobias, J., Springenberg, M. R., and Brox, T · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Cited alongside, same era.
Tiny imagenet visual recognition challenge
Le, Y. and Yang, X · 2015
Cited alongside, same era.
Epicflow: Edge-preserving interpolation of correspondences for optical flow
How well do self-supervised models transfer?
Ericsson, L., Gouk, H., and Hospedales, T. M · 2021
Later among the works it cites.
Provable guarantees for self-supervised deep learning with spectral contrastive loss
HaoChen, J. Z., Wei, C., Gaidon, A., and Ma, T · 2021
Later among the works it cites.
Towards the generalization of contrastive self-supervised learning
Huang, W., Yi, M., and Zhao, X · 2021
Later among the works it cites.
Understanding dimensional collapse in contrastive self-supervised learning
Jing, L., Vincent, P., LeCun, Y., and Tian, Y · 2021
Later among the works it cites.
Self-supervised learning with kernel dependence maximization
Li, Y., Pogodin, R., Sutherland, D. J., and Gretton, A · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Revaud, J., Weinzaepfel, P., Harchaoui, Z., and Schmid, C · 2015
Cited alongside, same era.
Deep learning , volume 1
Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Autoencoding beyond pixels using a learned similarity metric
Larsen, A. B. L., Sønderby, S. K., Larochelle, H., and Winther, O · 2016
Cited alongside, same era.
Zagoruyko, S. and Komodakis, N · 2016
Cited alongside, same era.
Unsupervised learning by predicting noise
Bojanowski, P. and Joulin, A · 2017
Cited alongside, same era.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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
Later among the works it cites.
Selfaugment: Automatic augmentation policies for self-supervised learning
Reed, C. J., Metzger, S., Srinivas, A., Darrell, T., and Keutzer, K · 2021
Later among the works it cites.
Understanding self-supervised learning dynamics without contrastive pairs
Tian, Y., Chen, X., and Ganguli, S · 2021
Later among the works it cites.
Mlp-mixer: An all-mlp architecture for vision
Tolstikhin, I. O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al · 2021
Later among the works it cites.
Understanding the behaviour of contrastive loss
Wang, F. and Liu, H · 2021
Later among the works it cites.
Improving performance of autoencoder-based network anomaly detection on nsl-kdd dataset
Xu, W., Jang-Jaccard, J., Singh, A., Wei, Y., and Sabrina, F · 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.
Understanding hard negatives in noise contrastive estimation
Zhang, W. and Stratos, K · 2021
Later among the works it cites.
Ressl: Relational self-supervised learning with weak augmentation
Zheng, M., You, S., Wang, F., Qian, C., Zhang, C., Wang, X., and Xu, C · 2021
Later among the works it cites.
Balestriero, R. and LeCun, Y · 2022
Later among the works it cites.
Guillotine regularization: Improving deep networks generalization by removing their head
Bordes, F., Balestriero, R., Garrido, Q., Bardes, A., and Vincent, P · 2022
Later among the works it cites.
On minimal variations for unsupervised representation learning
Cabannes, V., Bietti, A., and Balestriero, R · 2022
Later among the works it cites.
solo-learn: A library of self-supervised methods for visual representation learning
da Costa, V. G. T., Fini, E., Nabi, M., Sebe, N., and Ricci, E · 2022
Later among the works it cites.
Improving self-supervised learning by characterizing idealized representations
Dubois, Y., Hashimoto, T., Ermon, S., and Liang, P · 2022
Later among the works it cites.
Garrido, Q., Balestriero, R., Najman, L., and Lecun, Y · 2022
Later among the works it cites.
Investigating power laws in deep representation learning
Ghosh, A., Mondal, A. K., Agrawal, K. K., and Richards, B · 2022
Later among the works it cites.
Exploring the gap between collapsed & whitened features in self-supervised learning
He, B. and Ozay, M · 2022
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.
Self-adaptive training: Bridging supervised and self-supervised learning
Huang, L., Zhang, C., and Zhang, H · 2022
Later among the works it cites.
A path towards autonomous machine intelligence
LeCun, Y · 2022
Later among the works it cites.
A convnet for the 2020s
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S · 2022
Later among the works it cites.
Self-supervised learning with an information maximization criterion
Ozsoy, S., Hamdan, S., Arik, S. Ö., Yuret, D., and Erdogan, A. T · 2022
Later among the works it cites.
Crafting better contrastive views for siamese representation learning
Peng, X., Wang, K., Zhu, Z., Wang, M., and You, Y · 2022
Later among the works it cites.
On the pros and cons of momentum encoder in self-supervised visual representation learning
Pham, T., Zhang, C., Niu, A., Zhang, K., and Yoo, C. D · 2022
Later among the works it cites.
A simple data mixing prior for improving self-supervised learning
Ren, S., Wang, H., Gao, Z., He, S., Yuille, A., Zhou, Y., and Xie, C · 2022
Later among the works it cites.
Identity-disentangled adversarial augmentation for self-supervised learning
Yang, K., Zhou, T., Tian, X., and Tao, D · 2022
Later among the works it cites.
Decoupled contrastive learning
Yeh, C.-H., Hong, C.-Y., Hsu, Y.-C., Liu, T.-L., Chen, Y., and LeCun, Y · 2022
Later among the works it cites.
Dual temperature helps contrastive learning without many negative samples: Towards understanding and simplifying moco
Zhang, C., Zhang, K., Pham, T. X., Niu, A., Qiao, Z., Yoo, C. D., and Kweon, I. S · 2022
Later among the works it cites.