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Disentanglement Learning

A collection of resources on generative models which utilize generator functions that map low-dimensional latent codes to high-dimensional data outputs.

Introduction

The disentangled representation learning is first introduced in [Bengio et al. Representation learning: A review and new perspectives. PAMI, 2013]. This is to say that each scalar of the representation only encodes a single independent factor.

Literature

Related

Compositional Generalization: people exhibit the capacity to understand and produce a potentially infinite number of novel combinations of known components. As Chomsky said, to make “infinite use of finite means.”

https://blog.research.google/2020/03/measuring-compositional-generalization.html

独立的研究可能不是足够充分的,应该是在你的模型框架下去探究解耦的可能性,这应该是模型本身所应该具备的能力。