Independence Constrained Disentangled Representation Learning from Epistemological Perspective

• URL: http://arxiv.org/abs/2409.02672v2
• Date: Sat, 5 Oct 2024 11:32:35 GMT
• Title: Independence Constrained Disentangled Representation Learning from Epistemological Perspective
• Authors: Ruoyu Wang, Lina Yao,
• Abstract summary: Disentangled Representation Learning aims to improve the explainability of deep learning methods by training a data encoder that identifies semantically meaningful latent variables in the data generation process. There is no consensus regarding the objective of disentangled representation learning. We propose a novel method for disentangled representation learning by employing an integration of mutual information constraint and independence constraint.
• Score: 13.51102815877287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Disentangled Representation Learning aims to improve the explainability of deep learning methods by training a data encoder that identifies semantically meaningful latent variables in the data generation process. Nevertheless, there is no consensus regarding a universally accepted definition for the objective of disentangled representation learning. In particular, there is a considerable amount of discourse regarding whether should the latent variables be mutually independent or not. In this paper, we first investigate these arguments on the interrelationships between latent variables by establishing a conceptual bridge between Epistemology and Disentangled Representation Learning. Then, inspired by these interdisciplinary concepts, we introduce a two-level latent space framework to provide a general solution to the prior arguments on this issue. Finally, we propose a novel method for disentangled representation learning by employing an integration of mutual information constraint and independence constraint within the Generative Adversarial Network (GAN) framework. Experimental results demonstrate that our proposed method consistently outperforms baseline approaches in both quantitative and qualitative evaluations. The method exhibits strong performance across multiple commonly used metrics and demonstrates a great capability in disentangling various semantic factors, leading to an improved quality of controllable generation, which consequently benefits the explainability of the algorithm.

Related papers

• Self-Distilled Disentangled Learning for Counterfactual Prediction [49.84163147971955]
  We propose the Self-Distilled Disentanglement framework, known as $SD2$. Grounded in information theory, it ensures theoretically sound independent disentangled representations without intricate mutual information estimator designs. Our experiments, conducted on both synthetic and real-world datasets, confirm the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-06-09T16:58:19Z)
• Constrained Multiview Representation for Self-supervised Contrastive Learning [4.817827522417457]
  We introduce a novel approach predicated on representation distance-based mutual information (MI) for measuring the significance of different views. We harness multi-view representations extracted from the frequency domain, re-evaluating their significance based on mutual information.
  arXiv  Detail & Related papers  (2024-02-05T19:09:33Z)
• Variational Distillation for Multi-View Learning [104.17551354374821]
  We design several variational information bottlenecks to exploit two key characteristics for multi-view representation learning. Under rigorously theoretical guarantee, our approach enables IB to grasp the intrinsic correlation between observations and semantic labels.
  arXiv  Detail & Related papers  (2022-06-20T03:09:46Z)
• Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization [73.89239820192894]
  We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances. We present a novel framework for the generation of counterfactual examples.
  arXiv  Detail & Related papers  (2022-05-20T15:02:53Z)
• Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization [76.68866368409216]
  We propose learning to dynamically select discretization tightness conditioned on inputs. We show that dynamically varying tightness in communication bottlenecks can improve model performance on visual reasoning and reinforcement learning tasks.
  arXiv  Detail & Related papers  (2022-02-02T23:54:26Z)
• Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition [59.52434325897716]
  We propose a solution, named DMUE, to address the problem of annotation ambiguity from two perspectives. For the former, an auxiliary multi-branch learning framework is introduced to better mine and describe the latent distribution in the label space. For the latter, the pairwise relationship of semantic feature between instances are fully exploited to estimate the ambiguity extent in the instance space.
  arXiv  Detail & Related papers  (2021-04-01T03:21:57Z)
• Deep Clustering by Semantic Contrastive Learning [67.28140787010447]
  We introduce a novel variant called Semantic Contrastive Learning (SCL) It explores the characteristics of both conventional contrastive learning and deep clustering. It can amplify the strengths of contrastive learning and deep clustering in a unified approach.
  arXiv  Detail & Related papers  (2021-03-03T20:20:48Z)
• Modal Uncertainty Estimation via Discrete Latent Representation [4.246061945756033]
  We introduce a deep learning framework that learns the one-to-many mappings between the inputs and outputs, together with faithful uncertainty measures. Our framework demonstrates significantly more accurate uncertainty estimation than the current state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-25T05:29:34Z)

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.