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Found 4 papers, 3 papers with code
Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning
In the second phase, we combine this new knowledge with the previous network in an adaptation-retrospection phase to avoid forgetting and initialize a new expert with the knowledge of the old network.
Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training
The data augmentations used are of crucial importance to the quality of learned feature representations.
Continually Learning Self-Supervised Representations with Projected Functional Regularization
Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches.
Contrast Sensitivity Functions in Autoencoders
Three decades ago, Atick et al. suggested that human frequency sensitivity may emerge from the enhancement required for a more efficient analysis of retinal images.
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