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Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints
Evaluating adversarial robustness amounts to finding the minimum perturb...
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Contrastive Learning Inverts the Data Generating Process
Contrastive learning has recently seen tremendous success in self-superv...
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Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations
Feature visualizations such as synthetic maximally activating images are...
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On the surprising similarities between supervised and self-supervised models
How do humans learn to acquire a powerful, flexible and robust represent...
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EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy
EagerPy is a Python framework that lets you write code that automaticall...
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Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
We construct an unsupervised learning model that achieves nonlinear dise...
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Improving robustness against common corruptions by covariate shift adaptation
Today's state-of-the-art machine vision models are vulnerable to image c...
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Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences
Perceiving the world in terms of objects is a crucial prerequisite for r...
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The Notorious Difficulty of Comparing Human and Machine Perception
With the rise of machines to human-level performance in complex recognit...
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Shortcut Learning in Deep Neural Networks
Deep learning has triggered the current rise of artificial intelligence ...
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On Adaptive Attacks to Adversarial Example Defenses
Adaptive attacks have (rightfully) become the de facto standard for eval...
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Increasing the robustness of DNNs against image corruptions by playing the Game of Noise
The human visual system is remarkably robust against a wide range of nat...
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Learning From Brains How to Regularize Machines
Despite impressive performance on numerous visual tasks, Convolutional N...
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Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
The ability to detect objects regardless of image distortions or weather...
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Accurate, reliable and fast robustness evaluation
Throughout the past five years, the susceptibility of neural networks to...
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On Evaluating Adversarial Robustness
Correctly evaluating defenses against adversarial examples has proven to...
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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Convolutional Neural Networks (CNNs) are commonly thought to recognise o...
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Adversarial Vision Challenge
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitat...
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One-shot Texture Segmentation
We introduce one-shot texture segmentation: the task of segmenting an in...
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Robust Perception through Analysis by Synthesis
The intriguing susceptibility of deep neural networks to minimal input p...
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Trace your sources in large-scale data: one ring to find them all
An important preprocessing step in most data analysis pipelines aims to ...
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Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
Many machine learning algorithms are vulnerable to almost imperceptible ...
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Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models
Even todays most advanced machine learning models are easily fooled by a...
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Comment on "Biologically inspired protection of deep networks from adversarial attacks"
A recent paper suggests that Deep Neural Networks can be protected from ...
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Texture Synthesis Using Shallow Convolutional Networks with Random Filters
Here we demonstrate that the feature space of random shallow convolution...
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Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters
Neurons in higher cortical areas, such as the prefrontal cortex, are kno...
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