Equivariant Disentangled Transformation for Domain Generalization under Combination Shift

• URL: http://arxiv.org/abs/2208.02011v1
• Date: Wed, 3 Aug 2022 12:31:31 GMT
• Title: Equivariant Disentangled Transformation for Domain Generalization under Combination Shift
• Authors: Yivan Zhang, Jindong Wang, Xing Xie, Masashi Sugiyama
• Abstract summary: Combinations of domains and labels are not observed during training but appear in the test environment. We provide a unique formulation of the combination shift problem based on the concepts of homomorphism, equivariance, and a refined definition of disentanglement.
• Score: 91.38796390449504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning systems may encounter unexpected problems when the data distribution changes in the deployment environment. A major reason is that certain combinations of domains and labels are not observed during training but appear in the test environment. Although various invariance-based algorithms can be applied, we find that the performance gain is often marginal. To formally analyze this issue, we provide a unique algebraic formulation of the combination shift problem based on the concepts of homomorphism, equivariance, and a refined definition of disentanglement. The algebraic requirements naturally derive a simple yet effective method, referred to as equivariant disentangled transformation (EDT), which augments the data based on the algebraic structures of labels and makes the transformation satisfy the equivariance and disentanglement requirements. Experimental results demonstrate that invariance may be insufficient, and it is important to exploit the equivariance structure in the combination shift problem.

Related papers

• SEIS: Subspace-based Equivariance and Invariance Scores for Neural Representations [1.2431372110088466]
  We introduce SEIS, a subspace metric for analyzing layer-wise feature representations under geometric transformations.<n>Synthetic validation confirms that SEIS correctly recovers known transformations.<n>We show that multi-task learning induces synergistic gains in both properties at the shared encoder.
  arXiv  Detail & Related papers  (2026-02-03T22:38:08Z)
• Approximate equivariance via projection-based regularisation [1.5755923640031846]
  Non-equivariant models have regained attention, due to their better runtime performance and imperfect symmetries.<n>This has motivated the development of approximately equivariant models that strike a middle ground between respecting symmetries and fitting the data distribution.<n>We present a mathematical framework for computing the non-equivariance penalty exactly and efficiently in both the spatial and spectral domain.
  arXiv  Detail & Related papers  (2026-01-08T15:35:42Z)
• On Uncertainty Calibration for Equivariant Functions [11.063402372161859]
  We present a theory relating equivariance to uncertainty estimation.<n>We show how symmetry mismatch can result in miscalibration in both classification and regression.<n>We complement our theoretical framework with numerical experiments that clarify the relationship between equivariance and uncertainty.
  arXiv  Detail & Related papers  (2025-10-24T17:50:41Z)
• Symmetry and Generalisation in Machine Learning [0.0]
  We show that for any predictor that is not equivariant, there is an equivariant predictor with strictly lower test risk on all regression problems. We adopt an alternative perspective and formalise the common intuition that learning with invariant models reduces to a problem in terms of orbit representatives.
  arXiv  Detail & Related papers  (2025-01-07T15:14:58Z)
• Equivariant score-based generative models provably learn distributions with symmetries efficiently [7.90752151686317]
  Empirical studies have demonstrated that incorporating symmetries into generative models can provide better generalization and sampling efficiency. We provide the first theoretical analysis and guarantees of score-based generative models (SGMs) for learning distributions that are invariant with respect to some group symmetry.
  arXiv  Detail & Related papers  (2024-10-02T05:14:28Z)
• Almost Equivariance via Lie Algebra Convolutions [0.0]
  We provide a definition of almost equivariance and give a practical method for encoding it in models. Specifically, we define Lie algebra convolutions and demonstrate that they offer several benefits over Lie group convolutions. We prove two existence theorems, one showing the existence of almost isometries within bounded distance of isometries of a manifold, and another showing the converse for Hilbert spaces.
  arXiv  Detail & Related papers  (2023-10-19T21:31:11Z)
• Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance [31.875925637190328]
  In this paper, we present Banana, a Banach fixed-point network for equivariant segmentation with inter-part equivariance by construction. Our key insight is to iteratively solve a fixed-point problem, where point-part assignment labels and per-part SE(3)-equivariance co-evolve simultaneously. Our formulation naturally provides a strict definition of inter-part equivariance that generalizes to unseen inter-part configurations.
  arXiv  Detail & Related papers  (2023-05-25T17:59:32Z)
• Self-Supervised Learning for Group Equivariant Neural Networks [75.62232699377877]
  Group equivariant neural networks are the models whose structure is restricted to commute with the transformations on the input. We propose two concepts for self-supervised tasks: equivariant pretext labels and invariant contrastive loss. Experiments on standard image recognition benchmarks demonstrate that the equivariant neural networks exploit the proposed self-supervised tasks.
  arXiv  Detail & Related papers  (2023-03-08T08:11:26Z)
• Predicting Out-of-Domain Generalization with Neighborhood Invariance [59.05399533508682]
  We propose a measure of a classifier's output invariance in a local transformation neighborhood. Our measure is simple to calculate, does not depend on the test point's true label, and can be applied even in out-of-domain (OOD) settings. In experiments on benchmarks in image classification, sentiment analysis, and natural language inference, we demonstrate a strong and robust correlation between our measure and actual OOD generalization.
  arXiv  Detail & Related papers  (2022-07-05T14:55:16Z)
• Relaxing Equivariance Constraints with Non-stationary Continuous Filters [20.74154804898478]
  The proposed parameterization can be thought of as a building block to allow adjustable symmetry structure in neural networks. Compared to non-equivariant or strict-equivariant baselines, we experimentally verify that soft equivariance leads to improved performance in terms of test accuracy on CIFAR-10 and CIFAR-100 image classification tasks.
  arXiv  Detail & Related papers  (2022-04-14T18:08:36Z)
• Equivariance Discovery by Learned Parameter-Sharing [153.41877129746223]
  We study how to discover interpretable equivariances from data. Specifically, we formulate this discovery process as an optimization problem over a model's parameter-sharing schemes. Also, we theoretically analyze the method for Gaussian data and provide a bound on the mean squared gap between the studied discovery scheme and the oracle scheme.
  arXiv  Detail & Related papers  (2022-04-07T17:59:19Z)
• Commutative Lie Group VAE for Disentanglement Learning [96.32813624341833]
  We view disentanglement learning as discovering an underlying structure that equivariantly reflects the factorized variations shown in data. A simple model named Commutative Lie Group VAE is introduced to realize the group-based disentanglement learning. Experiments show that our model can effectively learn disentangled representations without supervision, and can achieve state-of-the-art performance without extra constraints.
  arXiv  Detail & Related papers  (2021-06-07T07:03:14Z)
• Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data [52.78581260260455]
  We propose a general method to construct a convolutional layer that is equivariant to transformations from any specified Lie group. We apply the same model architecture to images, ball-and-stick molecular data, and Hamiltonian dynamical systems.
  arXiv  Detail & Related papers  (2020-02-25T17:40:38Z)

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.