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A3T: Accuracy Aware Adversarial Training
Adversarial training has been empirically shown to be more prone to overfitting than standard training.
Impact of Adversarial Training on Robustness and Generalizability of Language Models
Specifically, we study the effect of pre-training data augmentation as well as training time input perturbations vs. embedding space perturbations on the robustness and generalization of transformer-based language models.
DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization
The recent growth of web video sharing platforms has increased the demand for systems that can efficiently browse, retrieve and summarize video content.
Can We Learn Heuristics For Graphical Model Inference Using Reinforcement Learning?
In this paper, we show that we can learn program heuristics, i. e., policies, for solving inference in higher order CRFs for the task of semantic segmentation, using reinforcement learning.
Structural Consistency and Controllability for Diverse Colorization
Colorizing a given gray-level image is an important task in the media and advertising industry.
