Matches in SemOpenAlex for { <https://semopenalex.org/work/W3211549568> ?p ?o ?g. }
- W3211549568 abstract "A good visual representation is an inference map from observations (images) to features (vectors) that faithfully reflects the hidden modularized generative factors (semantics). In this paper, we formulate the notion of good representation from a group-theoretic view using Higgins' definition of disentangled representation, and show that existing Self-Supervised Learning (SSL) only disentangles simple augmentation features such as rotation and colorization, thus unable to modularize the remaining semantics. To break the limitation, we propose an iterative SSL algorithm: Iterative Partition-based Invariant Risk Minimization (IP-IRM), which successfully grounds the abstract semantics and the group acting on them into concrete contrastive learning. At each iteration, IP-IRM first partitions the training samples into two subsets that correspond to an entangled group element. Then, it minimizes a subset-invariant contrastive loss, where the invariance guarantees to disentangle the group element. We prove that IP-IRM converges to a fully disentangled representation and show its effectiveness on various benchmarks. Codes are available at https://github.com/Wangt-CN/IP-IRM." @default.
- W3211549568 created "2021-11-22" @default.
- W3211549568 creator A5034434932 @default.
- W3211549568 creator A5042324027 @default.
- W3211549568 creator A5044447237 @default.
- W3211549568 creator A5057677588 @default.
- W3211549568 creator A5079865276 @default.
- W3211549568 date "2021-10-28" @default.
- W3211549568 modified "2023-10-16" @default.
- W3211549568 title "Self-Supervised Learning Disentangled Group Representation as Feature" @default.
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