2 citations · 2 across the 2 of their papers we have counts for
3 papers
Are Inherently Interpretable Models More Robust? A Study In Music Emotion Recognition
Katharina Hoedt, Arthur Flexer, Gerhard Widmer
One of the desired key properties of deep learning models is the ability to generalise to unseen samples. When provided with new samples that are (perceptually) similar to one or m…
Concept-Based Techniques for "Musicologist-friendly" Explanations in a Deep Music Classifier
Francesco Foscarin, Katharina Hoedt, Verena Praher +2
Current approaches for explaining deep learning systems applied to musical data provide results in a low-level feature space, e.g., by highlighting potentially relevant time-freque…
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…