137 citations · 227 across the 23 of their papers we have counts for
6 papers · 1 filter
Robust Upper Bounds for Adversarial Training
Dimitris Bertsimas, Xavier Boix, Kimberly Villalobos Carballo +1
Many state-of-the-art adversarial training methods for deep learning leverage upper bounds of the adversarial loss to provide security guarantees against adversarial attacks. Yet,…
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations
Akira Sakai, Taro Sunagawa, Spandan Madan +7
The training data distribution is often biased towards objects in certain orientations and illumination conditions. While humans have a remarkable capability of recognizing objects…
Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations
Avi Cooper, Xavier Boix, Daniel Harari +4
The capability of Deep Neural Networks (DNNs) to recognize objects in orientations outside the distribution of the training data is not well understood. We present evidence that DN…
The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions
Vanessa D'Amario, Sanjana Srivastava, Tomotake Sasaki +1
Datasets often contain input dimensions that are unnecessary to predict the output label, e.g. background in object recognition, which lead to more trainable parameters. Deep Neura…
In-distribution adversarial attacks on object recognition models using gradient-free search
Spandan Madan, Tomotake Sasaki, Hanspeter Pfister +2
Neural networks are susceptible to small perturbations in the form of 2D rotations and shifts, image crops, and even changes in object colors. Past works attribute these errors to…
How Modular Should Neural Module Networks Be for Systematic Generalization?
Vanessa D'Amario, Tomotake Sasaki, Xavier Boix
Neural Module Networks (NMNs) aim at Visual Question Answering (VQA) via composition of modules that tackle a sub-task. NMNs are a promising strategy to achieve systematic generali…