45 citations · 211 across the 23 of their papers we have counts for
6 papers · 1 filter
Aligning Multimodal Representations through an Information Bottleneck
Antonio Almudévar, José Miguel Hernández-Lobato, Sameer Khurana +2
Contrastive losses have been extensively used as a tool for multimodal representation learning. However, it has been empirically observed that their use is not effective to learn a…
Contrastive prediction strategies for unsupervised segmentation and categorization of phonemes and words
Santiago Cuervo, Maciej Grabias, Jan Chorowski +4
We investigate the performance on phoneme categorization and phoneme and word segmentation of several self-supervised learning (SSL) methods based on Contrastive Predictive Coding…
Information Retrieval for ZeroSpeech 2021: The Submission by University of Wroclaw
Jan Chorowski, Grzegorz Ciesielski, Jarosław Dzikowski +6
We present a number of low-resource approaches to the tasks of the Zero Resource Speech Challenge 2021. We build on the unsupervised representations of speech proposed by the organ…
Aligned Contrastive Predictive Coding
Jan Chorowski, Grzegorz Ciesielski, Jarosław Dzikowski +6
We investigate the possibility of forcing a self-supervised model trained using a contrastive predictive loss to extract slowly varying latent representations. Rather than producin…
Robust Training of Vector Quantized Bottleneck Models
Adrian Łańcucki, Jan Chorowski, Guillaume Sanchez +6
In this paper we demonstrate methods for reliable and efficient training of discrete representation using Vector-Quantized Variational Auto-Encoder models (VQ-VAEs). Discrete laten…
Deep Learning Classification With Noisy Labels
Guillaume Sanchez, Vincente Guis, Ricard Marxer +1
Deep Learning systems have shown tremendous accuracy in image classification, at the cost of big image datasets. Collecting such amounts of data can lead to labelling errors in the…