Summary of Improving Consistency Models with Generator-augmented Flows, by Thibaut Issenhuth et al.
Improving Consistency Models with Generator-Augmented Flows
by Thibaut Issenhuth, Sangchul Lee, Ludovic Dos Santos, Jean-Yves Franceschi, Chansoo Kim, Alain Rakotomamonjy
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 proposes a novel approach to consistency models, which imitate the multi-step sampling of score-based diffusion in a single forward pass of a neural network. The model can be learned through two methods: consistency distillation and consistency training. Consistency distillation relies on the true velocity field of the corresponding differential equation, approximated by a pre-trained neural network. In contrast, consistency training uses a single-sample Monte Carlo estimate of this velocity field. The paper shows that there is a discrepancy between consistency distillation and training that persists in the continuous-time limit. To address this issue, the authors propose a novel flow that transports noisy data towards their corresponding outputs derived from a consistency model. This flow reduces the previously identified discrepancy and noise-data transport cost, accelerating convergence and improving overall performance. The proposed method is evaluated using [insert relevant datasets/models/tasks], achieving state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn better by making them more consistent. It proposes a new way to teach models called consistency models that can be learned in two ways: distillation and training. Distillation uses pre-trained networks, while training estimates the velocity field using random samples. The problem is that these two methods don’t match perfectly, which makes it harder for the model to learn. To fix this, the authors suggest a new flow that moves noisy data towards the correct output. This flow reduces errors and helps the model learn faster and better. |
Keywords
» Artificial intelligence » Diffusion » Distillation » Neural network
