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We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation of quantization and entropy, which we anneal to their discrete counterparts throughout training. We showcase this method for two challenging applications: Image compression and neural network compression. While these tasks have typically been approached with different methods, our soft-to-hard quantization approach gives results competitive with the state-of-the-art for both.
Author Information
Eirikur Agustsson (ETH Zurich)
I am a PhD student at the [Computer Vision Lab](http://www.vision.ee.ethz.ch) of [ETH Zurich](https://www.ethz.ch/en.html), under the supervision of [Prof. Luc Van Gool](https://scholar.google.ch/citations?user=TwMib_QAAAAJ&hl=en&oi=ao). Previously, I received a MSc degree in Electrical Engineering and Information Technology from ETH Zurich and a double BSc degree in Mathematics and Electrical Engineering from the University of Iceland. My main research interests include deep learning for data compression, regression & classification.
Fabian Mentzer (ETH Zurich)
Michael Tschannen (ETH Zurich)
Lukas Cavigelli (ETH Zurich)
Radu Timofte (ETH Zurich)
Luca Benini (ETH Zurich)
Luc V Gool (Computer Vision Lab, ETH Zurich)
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2019 Poster: Gated CRF Loss for Weakly Supervised Semantic Image Segmentation »
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2021 : Spatial-Temporal Gated Transformersfor Efficient Video Processing »
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2021 Poster: Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations »
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2020 Poster: GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network »
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2020 Poster: Soft Contrastive Learning for Visual Localization »
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2020 Poster: DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation »
Alexandre Carlier · Martin Danelljan · Alexandre Alahi · Radu Timofte -
2018 Poster: Deep Generative Models for Distribution-Preserving Lossy Compression »
Michael Tschannen · Eirikur Agustsson · Mario Lucic -
2017 Poster: Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees »
Francesco Locatello · Michael Tschannen · Gunnar Raetsch · Martin Jaggi -
2016 Poster: Dynamic Filter Networks »
Xu Jia · Bert De Brabandere · Tinne Tuytelaars · Luc V Gool -
2014 Poster: Quantized Kernel Learning for Feature Matching »
Danfeng Qin · Xuanli Chen · Matthieu Guillaumin · Luc V Gool -
2014 Poster: Self-Adaptable Templates for Feature Coding »
Xavier Boix · Gemma Roig · Salomon Diether · Luc V Gool -
2011 Poster: Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities »
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