CLIP-Map: Structured Matrix Mapping for Parameter-Efficient CLIP Compression

TL;DR

CLIP-Map introduces a learnable matrix-based compression method for CLIP that preserves more information and outperforms existing select-based methods, especially at high compression ratios.

Contribution

The paper proposes CLIP-Map, a novel structured matrix mapping approach using learnable matrices and Kronecker factorization for efficient CLIP compression.

Findings

Outperforms select-based methods across various compression ratios.

Significant improvements under high compression settings.

Uses Diagonal Inheritance Initialization for better optimization.

Abstract

Contrastive Language-Image Pre-training (CLIP) has achieved widely applications in various computer vision tasks, e.g., text-to-image generation, Image-Text retrieval and Image captioning. However, CLIP suffers from high memory and computation cost, which prohibits its usage to the resource-limited application scenarios. Existing CLIP compression methods typically reduce the size of pre-trained CLIP weights by selecting their subset as weight inheritance for further retraining via mask optimization or important weight measurement. However, these select-based weight inheritance often compromises the feature presentation ability, especially on the extreme compression. In this paper, we propose a novel mapping-based CLIP compression framework, CLIP-Map. It leverages learnable matrices to map and combine pretrained weights by Full-Mapping with Kronecker Factorization, aiming to preserve as much information from the original weights as possible. To mitigate the optimization challenges introduced by the learnable mapping, we propose Diagonal Inheritance Initialization to reduce the distribution shifting problem for efficient and effective mapping learning. Extensive experimental results demonstrate that the proposed CLIP-Map outperforms select-based frameworks across various compression ratios, with particularly significant gains observed under high compression settings.