… Our proposed technique, referred to as Robust-Deep, increases the number of brain imaging
… Robust-Deep improves the robustness of deep learning models by decreasing the bias …

… We find that overfitting to the training set does in fact harm robust performance to a very …
the best robust test performance during training and the final robust test performance at the end …

… While much progress has been made in improving the performance of deep learning models
on altered or corrupted inputs, the results of this study and separate large-scale empirical …

… In this paper, we compare the recognition performance and robustness of two multimodal
emotion recognition models: deep canonical correlation analysis (DCCA) and bimodal …

… To tackle this challenge, we select a Deep Learning … architecture to enhance its performance.
The experimental evaluation … approach to investigate the robustness of our solution in the …

… To improve the robustness of deep learning based VAD models, noise-aware training (NAT) …
remarkable noise robustness. By using NAT proposed in this paper, performance is further …

… ), while the robustness is the ability to accept wide range of testing cases. In this paper, we
propose to increase the performance and robustness of a Deep Reinforcement Learning for …

… work, we measure the robustness of different approaches for image … Our results indicate that
the state-of-the-art deep-learning-… We plot the performance on those images as a function of …

… too much on detection performance while ignoring adversarial robustness. In view of practice,
it is often hard to predict what would boost a detection algorithm's performance the most, …

… In comparison, we observe a significant drop in performance when only fine-tuning on …
meta-learning algorithm for reweighting training examples and training more robust deep learning …