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We present VILLA, the first known effort on large-scale adversarial trainingfor vision-and-language (V+L) representation learning. VILLA consists of twotraining stages: (i) task-agnostic adversarial pre-training; followed by (ii)task-specific adversarial finetuning. Instead of adding adversarialperturbations on image pixels and textual tokens, we propose to performadversarial training in the embedding space of each modality. To enablelarge-scale training, we adopt the "free" adversarial training strategy, andcombine it with KL-divergence-based regularization to promote higher invariancein 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 VisualQuestion Answering, Visual Commonsense Reasoning, Image-Text Retrieval,Referring Expression Comprehension, Visual Entailment, and NLVR2.

Large-Scale Adversarial Training for Vision-and-Language Representation Learning