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Found 5 papers, 3 papers with code
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration
Existing methods for SFDA leverage entropy-minimization techniques which: (i) apply only to classification; (ii) destroy model calibration; and (iii) rely on the source model achieving a good level of feature-space class-separation in the target domain.
Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phases
In this work we present a style modelling system that uses an animation synthesis network to model motion content based on local motion phases.
Modularity Trumps Invariance for Compositional Robustness
In this work we develop a compositional image classification task where, given a few elemental corruptions, models are asked to generalize to compositions of these corruptions.
Unit-level surprise in neural networks
To adapt to changes in real-world data distributions, neural networks must update their parameters.
Deephys: Deep Electrophysiology, Debugging Neural Networks under Distribution Shifts
Deep Neural Networks (DNNs) often fail in out-of-distribution scenarios.
