Summary of Independence Constrained Disentangled Representation Learning From Epistemological Perspective, by Ruoyu Wang et al.
Independence Constrained Disentangled Representation Learning from Epistemological Perspective
by Ruoyu Wang, Lina Yao
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores Disentangled Representation Learning (DRL) techniques aimed at improving the explainability of deep learning methods. DRL aims to identify semantically meaningful latent variables in data generation processes. The study investigates the interrelationships between latent variables and introduces a two-level latent space framework, proposing a novel method that integrates mutual information constraint and independence constraint within the Generative Adversarial Network (GAN) framework. Experimental results demonstrate that this approach outperforms baseline methods in both quantitative and qualitative evaluations, exhibiting strong performance across multiple metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making deep learning models more understandable by identifying special patterns in data. It looks at how different patterns are related to each other. The researchers come up with a new way to do this using a combination of two methods: one that considers how much information is shared between patterns and another that makes sure these patterns are independent. They test their method on multiple datasets and show it works better than existing approaches, making it easier to control what the model generates. |
Keywords
» Artificial intelligence » Deep learning » Gan » Generative adversarial network » Latent space » Representation learning
