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Those two papers are about learning discrete representations from data by taking inspiration from vector quantization. Learning discrete representations using neural networks is challenging and can helpful for tasks such as compression, planning, reasoning, and can be potentially more interpretable than continuous ones. In the two papers use those learned discrete representations to build autoregressive generative models on image, sound, and video. The second paper (Generating Diverse High-Fidelity Images with VQ-VAE-2) is basically a sequel of the first (Neural Discrete Representation Learning) where they scale the models to bigger datasets and images (up to 1024x1024 resolution).
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## Monday 30 March 10-11:30am - Full-Resolution Residual Networks for Semantic Segmentation
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## Canceled Monday 16 March 10-11:30am - Full-Resolution Residual Networks for Semantic Segmentation
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**Replacement date: Monday 30 March 10-11:30am**
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**Replacement date for canceled meeting at Monday 16 March 10-11:30am**
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virtual meeting via dfnconf: https://conf.dfn.de/webapp/conference/97977564<br>
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alternative link, if dfn is down: https://us04web.zoom.us/j/433015211
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