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State-of-the-Art in Human Scanpath Prediction
The last years have seen a surge in models predicting the scanpaths of f...
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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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Closing the Generalization Gap in One-Shot Object Detection
Despite substantial progress in object detection and few-shot learning, ...
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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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Fast Differentiable Clipping-Aware Normalization and Rescaling
Rescaling a vector δ⃗∈ℝ^n to a desired length is a common operation in m...
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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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Towards causal generative scene models via competition of experts
Learning how to model complex scenes in a modular way with recombinable ...
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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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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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Pretraining boosts out-of-domain robustness for pose estimation
Deep neural networks are highly effective tools for human and animal pos...
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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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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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One-Shot Instance Segmentation
We tackle one-shot visual search by example for arbitrary object categor...
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Excessive Invariance Causes Adversarial Vulnerability
Despite their impressive performance, deep neural networks exhibit strik...
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A rotation-equivariant convolutional neural network model of primary visual cortex
Classical models describe primary visual cortex (V1) as a filter bank of...
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Generalisation in humans and deep neural networks
We compare the robustness of humans and current convolutional deep neura...
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Adversarial Vision Challenge
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitat...
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Diverse feature visualizations reveal invariances in early layers of deep neural networks
Visualizing features in deep neural networks (DNNs) can help understandi...
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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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Markerless tracking of user-defined features with deep learning
Quantifying behavior is crucial for many applications in neuroscience. V...
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One-Shot Segmentation in Clutter
We tackle the problem of one-shot segmentation: finding and segmenting a...
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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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Guiding human gaze with convolutional neural networks
The eye fixation patterns of human observers are a fundamental indicator...
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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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Neural system identification for large populations separating "what" and "where"
Neuroscientists classify neurons into different types that perform simil...
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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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Comparing deep neural networks against humans: object recognition when the signal gets weaker
Human visual object recognition is typically rapid and seemingly effortl...
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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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Synthesising Dynamic Textures using Convolutional Neural Networks
Here we present a parametric model for dynamic textures. The model is ba...
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Controlling Perceptual Factors in Neural Style Transfer
Neural Style Transfer has shown very exciting results enabling new forms...
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DeepGaze II: Reading fixations from deep features trained on object recognition
Here we present DeepGaze II, a model that predicts where people look in ...
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Preserving Color in Neural Artistic Style Transfer
This note presents an extension to the neural artistic style transfer al...
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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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A note on the evaluation of generative models
Probabilistic generative models can be used for compression, denoising, ...
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A Neural Algorithm of Artistic Style
In fine art, especially painting, humans have mastered the skill to crea...
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Generative Image Modeling Using Spatial LSTMs
Modeling the distribution of natural images is challenging, partly becau...
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A Generative Model of Natural Texture Surrogates
Natural images can be viewed as patchworks of different textures, where ...
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Texture Synthesis Using Convolutional Neural Networks
Here we introduce a new model of natural textures based on the feature s...
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Supervised learning sets benchmark for robust spike detection from calcium imaging signals
A fundamental challenge in calcium imaging has been to infer the timing ...
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Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet
Recent results suggest that state-of-the-art saliency models perform far...
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Inference and Mixture Modeling with the Elliptical Gamma Distribution
We study modeling and inference with the Elliptical Gamma Distribution (...
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How close are we to understanding image-based saliency?
Within the set of the many complex factors driving gaze placement, the p...
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