End-to-End Supermask Pruning: Learning to Prune Image Captioning Models

TL;DR

This paper introduces a novel end-to-end weight pruning method for image captioning models, achieving up to 95% sparsity and significant model size reduction while maintaining or improving performance.

Contribution

It presents the first comprehensive comparison of pruning methods for image captioning and proposes a new gradual sparsification technique based on weight sensitivity.

Findings

80-95% sparsity can match or outperform dense models

Up to 75% reduction in model size achieved

Models with high sparsity attain CIDEr scores >120 on MS-COCO

Abstract

With the advancement of deep models, research work on image captioning has led to a remarkable gain in raw performance over the last decade, along with increasing model complexity and computational cost. However, surprisingly works on compression of deep networks for image captioning task has received little to no attention. For the first time in image captioning research, we provide an extensive comparison of various unstructured weight pruning methods on three different popular image captioning architectures, namely Soft-Attention, Up-Down and Object Relation Transformer. Following this, we propose a novel end-to-end weight pruning method that performs gradual sparsification based on weight sensitivity to the training loss. The pruning schemes are then extended with encoder pruning, where we show that conducting both decoder pruning and training simultaneously prior to the encoder pruning provides good overall performance. Empirically, we show that an 80% to 95% sparse network (up to 75% reduction in model size) can either match or outperform its dense counterpart. The code and pre-trained models for Up-Down and Object Relation Transformer that are capable of achieving CIDEr scores >120 on the MS-COCO dataset but with only 8.7 MB and 14.5 MB in model size (size reduction of 96% and 94% respectively against dense versions) are publicly available at https://github.com/jiahuei/sparse-image-captioning.