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Found 6 papers, 2 papers with code
Improving Forward Compatibility in Class Incremental Learning by Increasing Representation Rank and Feature Richness
Consequently, RFR achieves dual objectives in backward and forward compatibility: minimizing feature extractor modifications and enhancing novel task performance, respectively.
Enhancing Contrastive Learning with Efficient Combinatorial Positive Pairing
In the past few years, contrastive learning has played a central role for the success of visual unsupervised representation learning.
Towards a Rigorous Analysis of Mutual Information in Contrastive Learning
Contrastive learning has emerged as a cornerstone in recent achievements of unsupervised representation learning.
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution
Since the introduction of deep learning, a wide scope of representation properties, such as decorrelation, whitening, disentanglement, rank, isotropy, and mutual information, have been studied to improve the quality of representation.
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Domain Generalization
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DR.CPO: Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR Occlusion
To improve the diversity of the whole-body object construction, we develop an iterative method that stochastically combines multiple objects observed from the real world into a single object.
Mutual Information Estimation as a Difference of Entropies for Unsupervised Representation Learning
In this work, we derive a principled non-contrastive method where mutual information is estimated as a difference of entropies and thus no need for negative sampling.
