In-Context Symmetries: Self-Supervised Learning through Contextual World Models

• URL: http://arxiv.org/abs/2405.18193v1
• Date: Tue, 28 May 2024 14:03:52 GMT
• Title: In-Context Symmetries: Self-Supervised Learning through Contextual World Models
• Authors: Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi Jaakkola, Stefanie Jegelka,
• Abstract summary: We propose to learn a general representation that can adapt to be invariant or equivariant to different transformations by paying attention to context. Our proposed algorithm, Contextual Self-Supervised Learning (ContextSSL), learns equivariance to all transformations.
• Score: 41.61360016455319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: At the core of self-supervised learning for vision is the idea of learning invariant or equivariant representations with respect to a set of data transformations. This approach, however, introduces strong inductive biases, which can render the representations fragile in downstream tasks that do not conform to these symmetries. In this work, drawing insights from world models, we propose to instead learn a general representation that can adapt to be invariant or equivariant to different transformations by paying attention to context -- a memory module that tracks task-specific states, actions, and future states. Here, the action is the transformation, while the current and future states respectively represent the input's representation before and after the transformation. Our proposed algorithm, Contextual Self-Supervised Learning (ContextSSL), learns equivariance to all transformations (as opposed to invariance). In this way, the model can learn to encode all relevant features as general representations while having the versatility to tail down to task-wise symmetries when given a few examples as the context. Empirically, we demonstrate significant performance gains over existing methods on equivariance-related tasks, supported by both qualitative and quantitative evaluations.

Related papers

• Unsupervised Representation Learning from Sparse Transformation Analysis [79.94858534887801]
  We propose to learn representations from sequence data by factorizing the transformations of the latent variables into sparse components. Input data are first encoded as distributions of latent activations and subsequently transformed using a probability flow model.
  arXiv  Detail & Related papers  (2024-10-07T23:53:25Z)
• Current Symmetry Group Equivariant Convolution Frameworks for Representation Learning [5.802794302956837]
  Euclidean deep learning is often inadequate for addressing real-world signals where the representation space is irregular and curved with complex topologies. We focus on the importance of symmetry group equivariant deep learning models and their realization of convolution-like operations on graphs, 3D shapes, and non-Euclidean spaces.
  arXiv  Detail & Related papers  (2024-09-11T15:07:18Z)
• Flow Factorized Representation Learning [109.51947536586677]
  We introduce a generative model which specifies a distinct set of latent probability paths that define different input transformations. We show that our model achieves higher likelihoods on standard representation learning benchmarks while simultaneously being closer to approximately equivariant models.
  arXiv  Detail & Related papers  (2023-09-22T20:15:37Z)
• Domain Generalization In Robust Invariant Representation [10.132611239890345]
  In this paper, we investigate the generalization of invariant representations on out-of-distribution data. We show that the invariant model learns unstructured latent representations that are robust to distribution shifts.
  arXiv  Detail & Related papers  (2023-04-07T00:58:30Z)
• Learning Symmetric Embeddings for Equivariant World Models [9.781637768189158]
  We propose learning symmetric embedding networks (SENs) that encode an input space (e.g. images) This network can be trained end-to-end with an equivariant task network to learn an explicitly symmetric representation. Our experiments demonstrate that SENs facilitate the application of equivariant networks to data with complex symmetry representations.
  arXiv  Detail & Related papers  (2022-04-24T22:31:52Z)
• Topographic VAEs learn Equivariant Capsules [84.33745072274942]
  We introduce the Topographic VAE: a novel method for efficiently training deep generative models with topographically organized latent variables. We show that such a model indeed learns to organize its activations according to salient characteristics such as digit class, width, and style on MNIST. We demonstrate approximate equivariance to complex transformations, expanding upon the capabilities of existing group equivariant neural networks.
  arXiv  Detail & Related papers  (2021-09-03T09:25:57Z)
• Improving Transformation Invariance in Contrastive Representation Learning [31.223892428863238]
  We introduce a training objective for contrastive learning that uses a novel regularizer to control how the representation changes under transformation. Second, we propose a change to how test time representations are generated by introducing a feature averaging approach that combines encodings from multiple transformations of the original input. Third, we introduce the novel Spirograph dataset to explore our ideas in the context of a differentiable generative process with multiple downstream tasks.
  arXiv  Detail & Related papers  (2020-10-19T13:49:29Z)
• Meta-Learning Symmetries by Reparameterization [63.85144439337671]
  We present a method for learning and encoding equivariances into networks by learning corresponding parameter sharing patterns from data. Our experiments suggest that it can automatically learn to encode equivariances to common transformations used in image processing tasks.
  arXiv  Detail & Related papers  (2020-07-06T17:59:54Z)
• On Compositions of Transformations in Contrastive Self-Supervised Learning [66.15514035861048]
  In this paper, we generalize contrastive learning to a wider set of transformations. We find that being invariant to certain transformations and distinctive to others is critical to learning effective video representations.
  arXiv  Detail & Related papers  (2020-03-09T17:56:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.