62 citations · 223 across the 11 of their papers we have counts for
11 papers
HARD: Hard Augmentations for Robust Distillation
Arne F. Nix, Max F. Burg, Fabian H. Sinz
Knowledge distillation (KD) is a simple and successful method to transfer knowledge from a teacher to a student model solely based on functional activity. However, current KD has a…
The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
Polina Turishcheva, Paul G. Fahey, Laura Hansel +10
Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual inpu…
Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions
Paweł A. Pierzchlewicz, R. James Cotton, Mohammad Bashiri +1
Due to depth ambiguities and occlusions, lifting 2D poses to 3D is a highly ill-posed problem. Well-calibrated distributions of possible poses can make these ambiguities explicit a…
Towards robust vision by multi-task learning on monkey visual cortex
Shahd Safarani, Arne Nix, Konstantin Willeke +5
Deep neural networks set the state-of-the-art across many tasks in computer vision, but their generalization ability to image distortions is surprisingly fragile. In contrast, the…
Factorized Neural Processes for Neural Processes: -Shot Prediction of Neural Responses
R. James Cotton, Fabian H. Sinz, Andreas S. Tolias
In recent years, artificial neural networks have achieved state-of-the-art performance for predicting the responses of neurons in the visual cortex to natural stimuli. However, the…
Learning From Brains How to Regularize Machines
Zhe Li, Wieland Brendel, Edgar Y. Walker +7
Despite impressive performance on numerous visual tasks, Convolutional Neural Networks (CNNs) --- unlike brains --- are often highly sensitive to small perturbations of their input…
A rotation-equivariant convolutional neural network model of primary visual cortex
Alexander S. Ecker, Fabian H. Sinz, Emmanouil Froudarakis +7
Classical models describe primary visual cortex (V1) as a filter bank of orientation-selective linear-nonlinear (LN) or energy models, but these models fail to predict neural respo…
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes
Mengye Ren, Renjie Liao, Raquel Urtasun +2
Normalization techniques have only recently begun to be exploited in supervised learning tasks. Batch normalization exploits mini-batch statistics to normalize the activations. Thi…
In All Likelihood, Deep Belief Is Not Enough
Lucas Theis, Sebastian Gerwinn, Fabian Sinz +1
Statistical models of natural stimuli provide an important tool for researchers in the fields of machine learning and computational neuroscience. A canonical way to quantitatively…
-nested symmetric distributions
Fabian Sinz, Matthias Bethge
Tractable generalizations of the Gaussian distribution play an important role for the analysis of high-dimensional data. One very general super-class of Normal distributions is the…
Natural Image Coding in V1: How Much Use is Orientation Selectivity?
Jan Eichhorn, Fabian Sinz, Matthias Bethge
Orientation selectivity is the most striking feature of simple cell coding in V1 which has been shown to emerge from the reduction of higher-order correlations in natural images in…