Probabilistic inference and o.o.d. or few-shot generalization benchmarking
- A note on the evaluation of generative models (L Theis, A Oord, M Bethge)
- Reliable attacks against black-box machine learning models (W Brendel, J Rauber, M Bethge)
- Shortcut learning in deep neural networks (R Geirhos, JH Jacobsen, C Michaelis, R Zemel, W Brendel, M Bethge, …)
- Foolbox: A python toolbox to benchmark the robustness of machine learning models (J Rauber, W Brendel, M Bethge)
- Generalisation in humans and deep neural networks (R Geirhos, CRM Temme, J Rauber, HH Schütt, M Bethge, FA Wichmann)
- Information-theoretic model comparison unifies saliency metrics (M Kümmerer, TSA Wallis, M Bethge)
- Saliency benchmarking made easy: Separating models, maps and metrics (M Kümmerer, TSA Wallis, M Bethge)
- Benchmarking spike rate inference in population calcium imaging (L Theis, P Berens, E Froudarakis, J Reimer, MR Rosón, T Baden, T Euler, …)
- Benchmarking robustness in object detection: Autonomous driving when winter is coming (C Michaelis, B Mitzkus, R Geirhos, E Rusak, O Bringmann, AS Ecker, …)
- If your data distribution shifts, use self-learning (E Rusak, S Schneider, G Pachitariu, L Eck, PV Gehler, O Bringmann, W Brendel, M Bethge)
- ImageNet-D: A new challenging robustness dataset inspired by domain adaptation (E Rusak, S Schneider, PV Gehler, O Bringmann, W Brendel, M Bethge)
- Benchmarking Unsupervised Object Representations for Video Sequences (MA Weis, K Chitta, Y Sharma, W Brendel, M Bethge, A Geiger, AS Ecker)
- In All Likelihood, Deep Belief Is Not Enough (L Theis, S Gerwinn, F Sinz, M Bethge)
- Engineering a less artificial intelligence (FH Sinz, X Pitkow, J Reimer, M Bethge, AS Tolias)
- Foolbox native: Fast adversarial attacks to benchmark the robustness of machine learning models in pytorch, tensorflow, and jax (J Rauber, R Zimmermann, M Bethge, W Brendel)
- Accurate, reliable and fast robustness evaluation (W Brendel, J Rauber, M Kümmerer, I Ustyuzhaninov, M Bethge)
- Factorial coding of natural images: how effective are linear models in removing higher-order dependencies? (M Bethge)
- Partial success in closing the gap between human and machine vision (R Geirhos, K Narayanappa, B Mitzkus, T Thieringer, M Bethge, …)
- Measuring the importance of temporal features in video saliency (M Tangemann, M Kümmerer, TSA Wallis, M Bethge)
- State-of-the-Art in Human Scanpath Prediction (M Kümmerer, M Bethge)