Summary of Interpretable Representation Learning From Videos Using Nonlinear Priors, by Marian Longa et al.
Interpretable Representation Learning from Videos using Nonlinear Priors
by Marian Longa, João F. Henriques
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 deep learning framework aims to learn interpretable representations of visual data, enabling machines to make decisions understandable to humans and improving generalization outside the training distribution. By specifying nonlinear priors for videos, the model learns latent variables that can generate videos of hypothetical scenarios not observed during training. The proposed approach extends the Variational Auto-Encoder (VAE) prior to an arbitrary nonlinear temporal Additive Noise Model (ANM), allowing it to describe a wide range of processes. A novel linearization method constructs a Gaussian Mixture Model (GMM) approximating the prior, and a numerically stable Monte Carlo estimate is derived for the KL divergence between the posterior and prior GMMs. The framework is validated on real-world physics videos, including a pendulum, mass on a spring, falling object, and pulsar. By specifying physical priors for each experiment, the correct variables are learned, enabling the generation of physically correct videos of hypothetical scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn how to make decisions that humans can understand. It creates a new way for computers to learn from data by using special rules (called priors) to help them make sense of what they see. The computer learns important details, like patterns in movement or behavior, and uses those to create videos of things that haven’t happened before. For example, it could show you what would happen if a ball were thrown differently or if an object moved faster or slower. |
Keywords
» Artificial intelligence » Deep learning » Encoder » Generalization » Mixture model
