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Found 8 papers, 4 papers with code
Task Arithmetic Through The Lens Of One-Shot Federated Learning
In this paper, we examine Task Arithmetic for multi-task learning by framing it as a one-shot Federated Learning problem.
Rethinking VLMs and LLMs for Image Classification
Visual Language Models (VLMs) are now increasingly being merged with Large Language Models (LLMs) to enable new capabilities, particularly in terms of improved interactivity and open-ended responsiveness.
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.
Deephys: Deep Electrophysiology, Debugging Neural Networks under Distribution Shifts
Deep Neural Networks (DNNs) often fail in out-of-distribution scenarios.
DeepPhase: Periodic Autoencoders for Learning Motion Phase Manifolds
Learning the spatial-temporal structure of body movements is a fundamental problem for character motion synthesis.
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.
Unit-level surprise in neural networks
To adapt to changes in real-world data distributions, neural networks must update their parameters.
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.
