CFASL: Composite Factor-Aligned Symmetry Learning for Disentanglement in Variational AutoEncoder

• URL: http://arxiv.org/abs/2401.08897v3
• Date: Tue, 12 Nov 2024 01:30:06 GMT
• Title: CFASL: Composite Factor-Aligned Symmetry Learning for Disentanglement in Variational AutoEncoder
• Authors: Hee-Jun Jung, Jaehyoung Jeong, Kangil Kim,
• Abstract summary: We propose a novel method, Composite Factor-Aligned Symmetry Learning (CFASL), which is integrated into VAEs for learning symmetry-based disentanglement. CFASL incorporates three novel features for learning symmetry-based disentanglement. CFASL demonstrates a significant improvement of disentanglement in single-factor change, and multi-factor change conditions.
• Score: 2.048226951354646
• License:
• Abstract: Symmetries of input and latent vectors have provided valuable insights for disentanglement learning in VAEs. However, only a few works were proposed as an unsupervised method, and even these works require known factor information in the training data. We propose a novel method, Composite Factor-Aligned Symmetry Learning (CFASL), which is integrated into VAEs for learning symmetry-based disentanglement in unsupervised learning without any knowledge of the dataset factor information. CFASL incorporates three novel features for learning symmetry-based disentanglement: 1) Injecting inductive bias to align latent vector dimensions to factor-aligned symmetries within an explicit learnable symmetry code-book 2) Learning a composite symmetry to express unknown factors change between two random samples by learning factor-aligned symmetries within the codebook 3) Inducing a group equivariant encoder and decoder in training VAEs with the two conditions. In addition, we propose an extended evaluation metric for multi-factor changes in comparison to disentanglement evaluation in VAEs. In quantitative and in-depth qualitative analysis, CFASL demonstrates a significant improvement of disentanglement in single-factor change, and multi-factor change conditions compared to state-of-the-art methods.

Related papers

• Approximate Equivariance in Reinforcement Learning [35.04248486334824]
  Equivariant neural networks have shown great success in reinforcement learning. In many problems, only approximate symmetry is present, which makes imposing exact symmetry inappropriate. We develop approximately equivariant algorithms in reinforcement learning.
  arXiv  Detail & Related papers  (2024-11-06T19:44:46Z)
• Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method [21.16129116282759]
  We introduce a new asymmetric learning paradigm based on coupled covariance eigenproblem (CCE) We formalize the asymmetric Nystr"om method through a finite sample approximation to speed up training.
  arXiv  Detail & Related papers  (2024-06-13T02:12:18Z)
• Symmetry Breaking and Equivariant Neural Networks [17.740760773905986]
  We introduce a novel notion of'relaxed equiinjection' We show how to incorporate this relaxation into equivariant multilayer perceptronrons (E-MLPs) The relevance of symmetry breaking is then discussed in various application domains.
  arXiv  Detail & Related papers  (2023-12-14T15:06:48Z)
• Learning Layer-wise Equivariances Automatically using Gradients [66.81218780702125]
  Convolutions encode equivariance symmetries into neural networks leading to better generalisation performance. symmetries provide fixed hard constraints on the functions a network can represent, need to be specified in advance, and can not be adapted. Our goal is to allow flexible symmetry constraints that can automatically be learned from data using gradients.
  arXiv  Detail & Related papers  (2023-10-09T20:22:43Z)
• ${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning [7.712824077083934]
  We focus on exploiting Euclidean symmetries inherent in certain cooperative multi-agent reinforcement learning problems. We design neural network architectures with symmetric constraints embedded as an inductive bias for multi-agent actor-critic methods.
  arXiv  Detail & Related papers  (2023-08-23T00:18:17Z)
• Oracle-Preserving Latent Flows [58.720142291102135]
  We develop a methodology for the simultaneous discovery of multiple nontrivial continuous symmetries across an entire labelled dataset. The symmetry transformations and the corresponding generators are modeled with fully connected neural networks trained with a specially constructed loss function. The two new elements in this work are the use of a reduced-dimensionality latent space and the generalization to transformations invariant with respect to high-dimensional oracles.
  arXiv  Detail & Related papers  (2023-02-02T00:13:32Z)
• Adaptive neighborhood Metric learning [184.95321334661898]
  We propose a novel distance metric learning algorithm, named adaptive neighborhood metric learning (ANML) ANML can be used to learn both the linear and deep embeddings. The emphlog-exp mean function proposed in our method gives a new perspective to review the deep metric learning methods.
  arXiv  Detail & Related papers  (2022-01-20T17:26:37Z)
• Symmetry-Aware Autoencoders: s-PCA and s-nlPCA [0.0]
  We introduce a novel machine learning embedding in the autoencoder, which uses spatial transformer networks and Siamese networks to account for continuous and discrete symmetries. The proposed symmetry-aware autoencoder is invariant to predetermined input transformations dictating the dynamics of the underlying physical system.
  arXiv  Detail & Related papers  (2021-11-04T14:22:19Z)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures [68.8204255655161]
  It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches. It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
  arXiv  Detail & Related papers  (2021-10-26T07:02:14Z)
• 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)
• Inverse Learning of Symmetries [71.62109774068064]
  We learn the symmetry transformation with a model consisting of two latent subspaces. Our approach is based on the deep information bottleneck in combination with a continuous mutual information regulariser. Our model outperforms state-of-the-art methods on artificial and molecular datasets.
  arXiv  Detail & Related papers  (2020-02-07T13:48:52Z)

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.