Summary of Learning From Pattern Completion: Self-supervised Controllable Generation, by Zhiqiang Chen et al.
Learning from Pattern Completion: Self-supervised Controllable Generation
by Zhiqiang Chen, Guofan Fan, Jinying Gao, Lei Ma, Bo Lei, Tiejun Huang, Shan Yu
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed self-supervised controllable generation (SCG) framework draws inspiration from the neural mechanisms that enable the brain’s associative power. It introduces an equivariant constraint to promote modularization in a autoencoder network, leading to functional specialization. This is followed by a self-supervised pattern completion approach for training controllable generation. The results demonstrate the effectiveness of the proposed framework in achieving modular processing and exhibiting brain-like features such as orientation selectivity and center-surround receptive fields. Moreover, through self-supervised training, associative generation capabilities emerge, showing excellent generalization ability to various tasks like painting, sketches, and ancient graffiti. Compared to ControlNet, SCG shows superior robustness in high-noise scenarios and promising scalability potential. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way of making computers generate images that are similar to what humans can do. It’s called self-supervised controllable generation (SCG). The idea is inspired by how our brains work when we see things like paintings or sketches, and how we can relate them to real-world objects. Computers usually need a lot of training data to do this kind of thing, but SCG doesn’t require that much data. Instead, it uses a special type of neural network that helps the computer understand different parts of an image and put them together in a way that looks like something our brain would recognize. |
Keywords
» Artificial intelligence » Autoencoder » Generalization » Neural network » Self supervised
