Summary of Abstracted Gaussian Prototypes For One-shot Concept Learning, by Chelsea Zou and Kenneth J. Kurtz
Abstracted Gaussian Prototypes for One-Shot Concept Learning
by Chelsea Zou, Kenneth J. Kurtz
First submitted to arxiv on: 30 Aug 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 framework introduces a cluster-based generative image segmentation approach inspired by the Omniglot Challenge, enabling one-shot learning for visual concept encoding. The framework infers Gaussian Mixture Model (GMM) parameters representing distinct topological subparts of visual concepts. By sampling new data from these parameters, the system generates augmented subparts to build robust prototypes for each concept, known as Abstracted Gaussian Prototypes (AGPs). This approach addresses one-shot classification tasks using a cognitively-inspired similarity metric and one-shot generative tasks through an AGP-VAE pipeline employing variational autoencoders. Human judges evaluate the generated examples and classes of visual concepts, finding them broadly indistinguishable from those created by humans. The framework achieves impressive, if not state-of-the-art, classification accuracy while being uniquely low in theoretical and computational complexity, operating standalone without relying on pre-training or knowledge engineering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to learn about images using just one example. This is called “one-shot learning” because it only needs one image to understand how to recognize similar images. The approach uses a special kind of math called Gaussian Mixture Models (GMMs) to break down an image into smaller parts, or “subparts”. These subparts are then used to create new, related images that are almost indistinguishable from those made by humans. |
Keywords
» Artificial intelligence » Classification » Image segmentation » Mixture model » One shot
