Discovering and recognizing the hidden factors behind observable data serves as one crucial step for machine learning algorithms to better understand the world. However, it still remains a challenging problem for current deep learning models which heavily rely on data representations. To solve this challenge, disentangled representation learning, as a recently cutting-edge topic in both academy and industry, aims at learning a disentangled representation for each object where different parts of the representation can express different (disentangled) semantics so as to improve the explainability and controllability of the machine learning models. Notably, it has achieved great success in diverse fields, such as image/video generation, recommender systems, and graph neural networks, covering a variety of areas ranging from computer vision to data-mining. In this tutorial, we will disseminate and promote the recent research achievements on disentangled representation learning as well as its applications, which is an exciting and fast-growing research direction in the general field of machine learning. We will also advocate novel, high-quality research findings, and innovative solutions to the challenging problems in disentangled representation learning. This tutorial consists of five parts. We first give a brief introduction to the research and industrial motivation, followed by discussions on basics, fundamentals and applications of disentangled representation learning. We will also discuss some recent advances covering disentangled graph representation learning and disentangled representation for recommendation. We finally share some of our insights on the trend for disentangled representation learning.
The tutorial can either be scheduled for quarter-slot or half-slot depending on the actual needs of the conference, and can be organized into the following 5 sections.
Target Audience and Prerequisites
This tutorial will be highly accessible to the whole AI community, including researchers, students and practitioners who are interested in disentangled representation learning and their applications in AI related tasks. The tutorial will be self-contained and designed for introductory and intermediate audiences. Although no special prerequisite knowledge is required to attend this tutorial, the audiences are supposed to have basic knowledge of machine learning, linear algebra, and calculus. In particular, audiences who have engaged in related topics (e.g., deep learning, reinforcement learning, information theory, causal inference) are welcome to have Q&A interaction during the tutorial.
Motivation, Relevance and Rationale
This tutorial is to disseminate and promote the recent research achievements on disentangled representation learning as well as its applications, which is an exciting and fast-growing research direction in the general field of machine learning. We will advocate novel, high-quality research findings, and innovative solutions to the challenging problems in disentangled representation learning. This topic is at the core of the scope of IJCAI, and is attractive to IJCAI audiences from both academia and industry. The objective of "Motivate and explain a topic of emerging importance for AI" will be best served by this tutorial.
We introduce the most recent updates and advances in disentangled representation learning during the past years. The discussion about disentangled representation learning will be scheduled from the following three aspects: i) basics and fundamentals of disentangled representation learning, ii) suitable application scenarios of disentangled representation learning and iii) advances of disentangled representation learning, including disentangled graph representation learning and disentangled representation learning for recommendation, etc.
Basics and fundamentals of disentangled representation learning
Current popular methods for learning disentangled representations can be roughly divided into the following categories: VAE-based, GAN-based, clustering-based methods, knowledge-guided methods.
Applications of disentangled representation learning
Disentangled representation learning easily finds its wide applications in various areas related to deep learning for its explainability and controllability. For example, in image generation, when we have disentangled representations for an image, it will be easy to generate the image with specific semantic attributes. Similarly, in recommendation, when we disentangle the click behavior of users within the latent representations, the recommender systems can provide the users with not only the items but also the potential reasons why the target users may like these items, thus improving the explainability in representation learning.
Recent advances of disentangled representation learning
Given that disentangled representation learning in visual data has been largely studied in the past few years, where we focus on more advances of disentangled representation learning, including disentangled representation learning for relational data structured via graphs and disentangled representation learning for user behaviors in recommendation, etc. For graph representation learning, the underlying reasons for edges connecting different nodes can be different, thus one advantage of learning disentangled representation is to discover the semantic meaning carried via these edges. For user behavior data which can be more complex and highly entangled, it will be more challenging and interesting to learn representations capable of disentangling the hidden patterns carried in the observed data.