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[2408.17251] Abstracted Gaussian Prototypes for True One-Shot Concept Learning
Summary
This paper presents a novel framework for one-shot learning in computer vision, utilizing Abstracted Gaussian Prototypes to enhance image segmentation and concept learning.
Why It Matters
The research addresses the challenge of one-shot learning, which is crucial for developing AI systems that can learn from minimal data. By proposing a framework that combines generative modeling with cognitive-inspired metrics, it advances the field of AI and machine learning, particularly in applications requiring rapid adaptation to new concepts.
Key Takeaways
- Introduces Abstracted Gaussian Prototypes for one-shot learning.
- Utilizes a cluster-based generative image segmentation framework.
- Achieves low theoretical and computational complexity.
- Demonstrates robust performance in classification and generative tasks.
- Advances understanding of learning systems with minimal examples.
Computer Science > Computer Vision and Pattern Recognition arXiv:2408.17251 (cs) [Submitted on 30 Aug 2024 (v1), last revised 26 Feb 2026 (this version, v2)] Title:Abstracted Gaussian Prototypes for True One-Shot Concept Learning Authors:Chelsea Zou, Kenneth J. Kurtz View a PDF of the paper titled Abstracted Gaussian Prototypes for True One-Shot Concept Learning, by Chelsea Zou and Kenneth J. Kurtz View PDF HTML (experimental) Abstract:We introduce a cluster-based generative image segmentation framework to encode higher-level representations of visual concepts based on one-shot learning inspired by the Omniglot Challenge. The inferred parameters of each component of a Gaussian Mixture Model (GMM) represent a distinct topological subpart of a visual concept. Sampling new data from these parameters generates augmented subparts to build a more robust prototype for each concept, i.e., the Abstracted Gaussian Prototype (AGP). This framework addresses one-shot classification tasks using a cognitively-inspired similarity metric and addresses one-shot generative tasks through a novel AGP-VAE pipeline employing variational autoencoders (VAEs) to generate new class variants. Results from human judges reveal that the generative pipeline produces novel examples and classes of visual concepts that are broadly indistinguishable from those made by humans. The proposed framework leads to impressive, but not state-of-the-art, classification accuracy; thus, the contribution is two-fold: 1) ...
