11 papers
BAT: Better Audio Transformer Guided by Convex Gated Probing
Houtan Ghaffari, Lukas Rauch, Christoph Scholz +1
Probing is widely adopted in computer vision to faithfully evaluate self-supervised learning (SSL) embeddings, as finetuning may misrepresent their inherent quality. In contrast, a…
Unmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classification
Lukas Rauch, René Heinrich, Houtan Ghaffari +4
Although probing frozen models has become a standard evaluation paradigm, self-supervised learning in audio defaults to fine-tuning when pursuing state-of-the-art on AudioSet. A ke…
Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent
Björn Hoppmann, Christoph Scholz
Humans are highly effective at utilizing prior knowledge to adapt to novel tasks, a capability that standard machine learning models struggle to replicate due to their reliance on…
Graph Reinforcement Learning for Power Grids: A Comprehensive Survey
Mohamed Hassouna, Clara Holzhüter, Pawel Lytaev +3
The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in…
Can Masked Autoencoders Also Listen to Birds?
Lukas Rauch, René Heinrich, Ilyass Moummad +3
Masked Autoencoders (MAEs) learn rich semantic representations in audio classification through an efficient self-supervised reconstruction task. However, general-purpose models fai…
Adversarial Training Improves Generalization Under Distribution Shifts in Bird Sound Classification
René Heinrich, René Heinrich, Lukas Rauch +5
Adversarial training is a promising strategy for enhancing robustness against adversarial attacks, but its impact on generalization under substantial distribution shifts in audio c…