
Representation learning from videos inthewild: An objectcentric approach
We propose a method to learn image representations from uncurated videos...
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On Robustness and Transferability of Convolutional Neural Networks
Modern deep convolutional networks (CNNs) are often criticized for not g...
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HighFidelity Generative Image Compression
We extensively study how to combine Generative Adversarial Networks and ...
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Learning Better Lossless Compression Using Lossy Compression
We leverage the powerful lossy image compression algorithm BPG to build ...
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Automatic Shortcut Removal for SelfSupervised Representation Learning
In selfsupervised visual representation learning, a feature extractor i...
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WeaklySupervised Disentanglement Without Compromises
Intelligent agents should be able to learn useful representations by obs...
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SelfSupervised Learning of VideoInduced Visual Invariances
We propose a general framework for selfsupervised learning of transfera...
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Semantic Bottleneck Scene Generation
Coupling the highfidelity generation capabilities of labelconditional ...
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On Mutual Information Maximization for Representation Learning
Many recent methods for unsupervised or selfsupervised representation l...
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Disentangling Factors of Variation Using Few Labels
Learning disentangled representations is considered a cornerstone proble...
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HighFidelity Image Generation With Fewer Labels
Deep generative models are becoming a cornerstone of modern machine lear...
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Recent Advances in AutoencoderBased Representation Learning
Learning useful representations with little or no supervision is a key c...
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Practical Full Resolution Learned Lossless Image Compression
We propose the first practical learned lossless image compression system...
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Deep Generative Models for DistributionPreserving Lossy Compression
We propose and study the problem of distributionpreserving lossy compre...
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Born Again Neural Networks
Knowledge distillation (KD) consists of transferring knowledge from one ...
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Generative Adversarial Networks for Extreme Learned Image Compression
We propose a framework for extreme learned image compression based on Ge...
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Towards Image Understanding from Deep Compression without Decoding
Motivated by recent work on deep neural network (DNN)based image compre...
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Conditional Probability Models for Deep Image Compression
Deep Neural Networks trained as image autoencoders have recently emerge...
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StrassenNets: Deep learning with a multiplication budget
A large fraction of the arithmetic operations required to evaluate deep ...
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Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
Greedy optimization methods such as Matching Pursuit (MP) and FrankWolf...
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SofttoHard Vector Quantization for EndtoEnd Learning Compressible Representations
We present a new approach to learn compressible representations in deep ...
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A Unified Optimization View on Generalized Matching Pursuit and FrankWolfe
Two of the most fundamental prototypes of greedy optimization are the ma...
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Deep Structured Features for Semantic Segmentation
We propose a highly structured neural network architecture for semantic ...
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Discrete Deep Feature Extraction: A Theory and New Architectures
First steps towards a mathematical theory of deep convolutional neural n...
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Pursuits in Structured NonConvex Matrix Factorizations
Efficiently representing real world data in a succinct and parsimonious ...
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Nonparametric Nearest Neighbor Random Process Clustering
We consider the problem of clustering noisy finitelength observations o...
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Subspace clustering of dimensionalityreduced data
Subspace clustering refers to the problem of clustering unlabeled highd...
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Michael Tschannen
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Research Assistant at ETH Zurich since 2014, Applied Scientist Intern at Amazon Web Services 2017, Research Intern (civil service) at University Hospital Balgrist 2014, Research Intern (civil service) at ARTORG Center for Computer Aided Surgery 2012, Research Intern at ARTORG Center for Computer Aided Surgery 2011