Training-Adaptive Convolutional Sparse Coding via Information Bottleneck for Robust Visual Representation
- 类型：arxiv
- 标识：2609.19122
- 链接：https://arxiv.org/abs/2609.19122
- 主分类：engineering
- 形态：method
- TLDR：Visual signals require compact yet sufficient representations for robust downstream prediction. Convolutional sparse coding (CSC) provides an explicit mechanism for suppressing redundant components while preserving signal content, but its sparsity coefficient is typically fixed and manually selected. We propose a training-adaptive convolutional sparse coding framework for robust visual signal representation. Specifically, we unfold the CSC optimization with the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and treat the sparsity coefficient as a differentiable variable jointly learne
- 待LLM分类：否
- 来源文件：
- /inbox/tom/_candidates/2026-09-21-agent-rag-longcontext-candidates.json