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Adaptive Convolutional Sparse Coding via Information Bottleneck for Robust Visual Signal Representation

Paper recorded by Signals 4 on 2026-09-16 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-16 on arXiv · recorded by Signals 4 on 2026-09-17

Category: cs.CV · 计算机视觉 · first seen 2026-09-17

Abstract

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 an adaptive convolutional sparse coding framework for robust visual signal representati

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#18 most recent of 237 cs.CV papers we have recorded · ↑ newer: In-Context Robot Learning with VLM Agents · ↓ older: Track, Articulate, Act: Generating Articulation from Casual Human Vide
Cite this page: Adaptive Convolutional Sparse Coding via Information Bottleneck for Robust Visual Signal Representation: the #18 most recent of 237 cs.CV papers we have recorded (as of 2026-09-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/adaptive-convolutional-sparse-coding-via-information-bottleneck-for-robust-visua.html
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