3 papers
cs.LG2026
WeightCLIP: Aligning Datasets and Models for Weight Space Learning
Aron Asefaw, Konstantinos Tzevelekakis, Damian Falk +2
Weight space learning aims to learn representations of neural network (NN) weights, enabling different downstream tasks. Existing approaches show promising performance, but lacking…
cs.LG2025
Learning Model Representations Using Publicly Available Model Hubs
Damian Falk, Konstantin Schürholt, Konstantinos Tzevelekakis +2
The weights of neural networks have emerged as a novel data modality, giving rise to the field of weight space learning. A central challenge in this area is that learning meaningfu…
cs.CV2024
Sun Off, Lights On: Photorealistic Monocular Nighttime Simulation for Robust Semantic Perception
Konstantinos Tzevelekakis, Shutong Zhang, Luc Van Gool +1
Nighttime scenes are hard to semantically perceive with learned models and annotate for humans. Thus, realistic synthetic nighttime data become all the more important for learning…