Search Results for author: Steffen Schneider

Found 11 papers, 9 papers with code

Learnable latent embeddings for joint behavioral and neural analysis

2 code implementations1 Apr 2022 Steffen Schneider, Jin Hwa Lee, Mackenzie Weygandt Mathis

Mapping behavioral actions to neural activity is a fundamental goal of neuroscience.

If your data distribution shifts, use self-learning

1 code implementation27 Apr 2021 Evgenia Rusak, Steffen Schneider, George Pachitariu, Luisa Eck, Peter Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge

We demonstrate that self-learning techniques like entropy minimization and pseudo-labeling are simple and effective at improving performance of a deployed computer vision model under systematic domain shifts.

 Ranked #1 on Unsupervised Domain Adaptation on ImageNet-C (using extra training data)

Robust classification Self-Learning +1

A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives

1 code implementation1 Sep 2020 Alexander Mathis, Steffen Schneider, Jessy Lauer, Mackenzie W. Mathis

Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem.

vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations

3 code implementations ICLR 2020 Alexei Baevski, Steffen Schneider, Michael Auli

We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task.

Ranked #2 on Speech Recognition on TIMIT (using extra training data)

General Classification Self-Supervised Learning +2

wav2vec: Unsupervised Pre-training for Speech Recognition

5 code implementations11 Apr 2019 Steffen Schneider, Alexei Baevski, Ronan Collobert, Michael Auli

Our experiments on WSJ reduce WER of a strong character-based log-mel filterbank baseline by up to 36% when only a few hours of transcribed data is available.

Ranked #5 on Speech Recognition on TIMIT (using extra training data)

General Classification Speech Recognition +1

Context-based Normalization of Histological Stains using Deep Convolutional Features

1 code implementation14 Aug 2017 Daniel Bug, Steffen Schneider, Anne Grote, Eva Oswald, Friedrich Feuerhake, Julia Schüler, Dorit Merhof

While human observers are able to cope with variations in color and appearance of histological stains, digital pathology algorithms commonly require a well-normalized setting to achieve peak performance, especially when a limited amount of labeled data is available.

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