no code implementations • NeurIPS 2020 • Matthew Painter, Jonathon Hare, Adam Prugel-Bennett
In this work we empirically show that linear disentangled representations are not generally present in standard VAE models and that they instead require altering the loss landscape to induce them.
5 code implementations • 27 Feb 2020 • Ethan Harris, Antonia Marcu, Matthew Painter, Mahesan Niranjan, Adam Prügel-Bennett, Jonathon Hare
Finally, we show that a consequence of the difference between interpolating MSDA such as MixUp and masking MSDA such as FMix is that the two can be combined to improve performance even further.
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on Fashion-MNIST
2 code implementations • 10 Sep 2018 • Ethan Harris, Matthew Painter, Jonathon Hare
We introduce torchbearer, a model fitting library for pytorch aimed at researchers working on deep learning or differentiable programming.