Michael Tschannen
31 papers · 2016–2025 · 8 conferences · across top CS/AI conferences
Achievements
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π Renaissance Researcher (7) π Interdisciplinary Bridge π Academic Marathon (9) π Conference Polyglot (8) πΊοΈ Taxonomy Completionist (54)
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Taxonomy Completionist
(54)
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Keyword Pioneer
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Hot Topic Early Bird
π₯
Mega-Team
(43)
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Keyword Champion
(3)
π€
Dynamic Duo
(10)
π
Triple Crown
β‘
Prolific Year
(5)
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Conference Pioneer
π
Century Club
(31)
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Trend Setter
ποΈ
Keyword Collector
(111)
π₯
Unstoppable
(6)
Conferences
CVPR (8)
ICML (7)
NIPS (6)
ICLR (5)
ICCV (2)
AISTATS (1)
ECCV (1)
WACV (1)
Top co-authors
Keywords
representation learning
(5)
transfer learning
(4)
convolutional neural network
(4)
self-supervised learning
(4)
image compression
(4)
entropy coding
(3)
vision-language model
(3)
image classification
(3)
lossy compression
(3)
image captioning
(3)
generative adversarial network
(3)
neural network
(3)
model scaling
(3)
matching pursuit
(2)
probabilistic model
(2)
contrastive learning
(2)
out-of-distribution generalization
(2)
few-shot learning
(2)
vision transformer
(2)
visual question answering
(2)
Papers
JetFormer: An autoregressive generative model of raw images and text
ICLR 2025
LocCa: Visual Pretraining with Location-aware Captioners
NIPS 2024
Finite Scalar Quantization: VQ-VAE Made Simple
ICLR 2024
GIVT: Generative Infinite-Vocabulary Transformers
ECCV 2024
On Scaling Up a Multilingual Vision and Language Model
CVPR 2024
CLIPPO: Image-and-Language Understanding From Pixels Only
CVPR 2023
Scaling Vision Transformers to 22 Billion Parameters
ICML 2023
Image Captioners Are Scalable Vision Learners Too
NIPS 2023
M2T: Masking Transformers Twice for Faster Decoding
ICCV 2023
FlexiViT: One Model for All Patch Sizes
CVPR 2023
Representation Learning From Videos In-the-Wild: An Object-Centric Approach
WACV 2021
On Robustness and Transferability of Convolutional Neural Networks
CVPR 2021
Self-Supervised Learning of Video-Induced Visual Invariances
CVPR 2020
Learning Better Lossless Compression Using Lossy Compression
CVPR 2020
Disentangling Factors of Variations Using Few Labels
ICLR 2020
On Mutual Information Maximization for Representation Learning
ICLR 2020
High-Fidelity Generative Image Compression
NIPS 2020
Weakly-Supervised Disentanglement Without Compromises
ICML 2020
Automatic Shortcut Removal for Self-Supervised Representation Learning
ICML 2020
Practical Full Resolution Learned Lossless Image Compression
CVPR 2019
Generative Adversarial Networks for Extreme Learned Image Compression
ICCV 2019
High-Fidelity Image Generation With Fewer Labels
ICML 2019
StrassenNets: Deep Learning with a Multiplication Budget
ICML 2018
Towards Image Understanding from Deep Compression Without Decoding
ICLR 2018
Deep Generative Models for Distribution-Preserving Lossy Compression
NIPS 2018
Born Again Neural Networks
ICML 2018
Conditional Probability Models for Deep Image Compression
CVPR 2018
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
NIPS 2017
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
NIPS 2017
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe
AISTATS 2017
Discrete Deep Feature Extraction: A Theory and New Architectures
ICML 2016