5 citations · 13 across the 13 of their papers we have counts for
6 papers · 1 filter
Cleaning the Pool: Progressive Filtering of Unlabeled Pools in Deep Active Learning
Denis Huseljic, Marek Herde, Lukas Rauch +2
Existing active learning (AL) strategies capture fundamentally different notions of data value, e.g., uncertainty or representativeness. Consequently, the effectiveness of strategi…
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…
Hashing-Baseline: Rethinking Hashing in the Age of Pretrained Models
Ilyass Moummad, Kawtar Zaher, Lukas Rauch +1
Information retrieval with compact binary embeddings, also referred to as hashing, is crucial for scalable fast search applications, yet state-of-the-art hashing methods require ex…
Adversarial Training Improves Generalization Under Distribution Shifts in Bird Sound Classification
René Heinrich, Lukas Rauch, Raphael Schwinger +4
Adversarial training is a promising strategy for enhancing robustness against adversarial attacks, but its impact on generalization under substantial distribution shifts in audio c…
No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success
Lukas Rauch, Moritz Wirth, Denis Huseljic +3
The advent of large language models (LLMs) capable of producing general-purpose representations lets us revisit the practicality of deep active learning (AL): By leveraging frozen…
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…