Additive Decoders for Latent Variables Identification

Explore a comprehensive lecture on additive decoders for latent variables identification presented by Sébastien Lachapelle from Valence Labs. Delve into the challenges of latent variables identification and "out-of-support" image generation in representation learning. Discover how additive decoders, similar to those used in object-centric representation learning (OCRL), can solve these problems for images decomposable into object-specific components. Learn about the conditions for identifying latent variable blocks and the theoretical foundations for nonlinear independent component analysis (ICA). Examine the concept of Cartesian-product extrapolation and its potential for generating novel images by recombining observed factors. Follow the lecture's progression through topics such as disentanglement, identifiability, sufficient nonlinearity, and extrapolation. Gain insights into the crucial role of additivity in both identifiability and extrapolation, supported by empirical evidence from simulated data.

- Discussant Slide + Introduction
- Additive Decoders
- Disentanglement
- Identifiability
- Sufficient Nonlinearity
- Extrapolation
- Conclusions