2 citations · 2 across the 3 of their papers we have counts for
4 papers · 1 filter
Defending a Music Recommender Against Hubness-Based Adversarial Attacks
Katharina Hoedt, Arthur Flexer, Gerhard Widmer
Adversarial attacks can drastically degrade performance of recommenders and other machine learning systems, resulting in an increased demand for defence mechanisms. We present a ne…
On the Veracity of Local, Model-agnostic Explanations in Audio Classification: Targeted Investigations with Adversarial Examples
Verena Praher, Katharina Prinz, Arthur Flexer +1
Local explanation methods such as LIME have become popular in MIR as tools for generating post-hoc, model-agnostic explanations of a model's classification decisions. The basic ide…
The Impact of Label Noise on a Music Tagger
Katharina Prinz, Arthur Flexer, Gerhard Widmer
We explore how much can be learned from noisy labels in audio music tagging. Our experiments show that carefully annotated labels result in highest figures of merit, but even high…
End-to-End Adversarial White Box Attacks on Music Instrument Classification
Katharina Prinz, Arthur Flexer
Small adversarial perturbations of input data are able to drastically change performance of machine learning systems, thereby challenging the validity of such systems. We present t…