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Large-Scale Adversarial Training for Vision-and-Language Representation Learning
Zhe Gan · Yen-Chun Chen · Linjie Li · Chen Zhu · Yu Cheng · Jingjing Liu

Tue Dec 08 09:00 PM -- 11:00 PM (PST) @ Poster Session 2 #676

We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning. VILLA consists of two training stages: (i) task-agnostic adversarial pre-training; followed by (ii) task-specific adversarial finetuning. Instead of adding adversarial perturbations on image pixels and textual tokens, we propose to perform adversarial training in the embedding space of each modality. To enable large-scale training, we adopt the ``free'' adversarial training strategy, and combine it with KL-divergence-based regularization to promote higher invariance in the embedding space. We apply VILLA to current best-performing V+L models, and achieve new state of the art on a wide range of tasks, including Visual Question Answering, Visual Commonsense Reasoning, Image-Text Retrieval, Referring Expression Comprehension, Visual Entailment, and NLVR2.

Author Information

Zhe Gan (Microsoft)
Yen-Chun Chen (Microsoft)
Linjie Li (Microsoft)
Chen Zhu (University of Maryland, College Park)
Yu Cheng (Microsoft)
Jingjing Liu (Microsoft)

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