Self-Supervised Learning of Audio Representations from Permutations with Differentiable Ranking

no code implementations • 17 Mar 2021 • Andrew N Carr, Quentin Berthet, Mathieu Blondel, Olivier Teboul, Neil Zeghidour

Second, we show that inverting permutations is a meaningful pretext task for learning audio representations in an unsupervised fashion.

Classification General Classification +1

Shuffle to Learn: Self-supervised learning from permutations via differentiable ranking

no code implementations • 1 Jan 2021 • Andrew N Carr, Quentin Berthet, Mathieu Blondel, Olivier Teboul, Neil Zeghidour

In particular, we also improve music understanding by reordering spectrogram patches in the frequency space, as well as video classification by reordering frames along the time axis.

General Classification Self-Supervised Learning +1