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Found 6 papers, 3 papers with code
When does Privileged Information Explain Away Label Noise?
Leveraging privileged information (PI), or features available during training but not at test time, has recently been shown to be an effective method for addressing label noise.
A neural anisotropic view of underspecification in deep learning
In this work, we propose to study this problem from a geometric perspective with the aim to understand two key characteristics of neural network solutions in underspecified settings: how is the geometry of the learned function related to the data representation?
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness
In this article, we provide an in-depth review of the field of adversarial robustness in deep learning, and give a self-contained introduction to its main notions.
Neural Anisotropy Directions
In this work, we analyze the role of the network architecture in shaping the inductive bias of deep classifiers.
Hold me tight! Influence of discriminative features on deep network boundaries
In this work, we borrow tools from the field of adversarial robustness, and propose a new perspective that relates dataset features to the distance of samples to the decision boundary.
Forward-Backward Splitting for Optimal Transport based Problems
Experiments show that the proposed method leads to a significant improvement in terms of speed and performance with respect to the state of the art for domain adaptation on a continually rotating distribution coming from the standard two moon dataset.
