Before revision:  In this work, we actively exploit the training data to improve the robustness and interpretability of deep neural networks , using the information from nearest training examples to aid the prediction both during training and testing.
After revision:  In this work, we actively exploit the training data , using the information from nearest training examples to aid the prediction both during training and testing.