4 accepted papers
We propose the Flow Stochastic Segmentation Network (Flow-SSN), a generative model for probabilistic segmentation featuring discrete-time autoregressive and modern continuous-time flow parameterisations. We prove fundamental limitations of the low-rank parameterisation of previous methods and show t…
We investigate the prominent class of fair representation learning methods for bias mitigation. Using causal reasoning to define and formalise different sources of dataset bias, we reveal important implicit assumptions inherent to these methods. We prove fundamental limitations on fair representatio…
Despite the constant development of new bias mitigation methods for machine learning, no method consistently succeeds, and a fundamental question remains unanswered: when and why do bias mitigation techniques fail? In this paper, we hypothesise that a key factor may be the often-overlooked but cruci…
Despite significant advancements in deep learning for vision and natural language, unsupervised domain adaptation in audio remains relatively unexplored. We, in part, attribute this to the lack of an appropriate benchmark dataset. To address this gap, we present Synthia’s melody, a novel audio data…