b'ETH Zurich'

research

∙ 01/19/2022

Near-Optimal Sparse Allreduce for Distributed Deep Learning

Communication overhead is one of the major obstacles to train large deep...

Shigang Li, et al. ∙

research

∙ 06/10/2020

Rendering Natural Camera Bokeh Effect with Deep Learning

Bokeh is an important artistic effect used to highlight the main object ...

Andrey Ignatov, et al. ∙

research

∙ 06/30/2020

Data Movement Is All You Need: A Case Study on Optimizing Transformers

Transformers have become widely used for language modeling and sequence ...

Andrei Ivanov, et al. ∙

research

∙ 05/18/2020

Deep Learning for Post-Processing Ensemble Weather Forecasts

Quantifying uncertainty in weather forecasts typically employs ensemble ...

Peter Grönquist, et al. ∙

research

∙ 04/30/2020

Unsupervised Lesion Detection via Image Restoration with a Normative Prior

Unsupervised lesion detection is a challenging problem that requires acc...

Xiaoran Chen, et al. ∙

research

∙ 11/15/2019

Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks

In the past few years, deep learning has transformed artificial intellig...

Thomas Mesnard, et al. ∙

research

∙ 12/14/2019

Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes

The main requisite for fine-grained recognition task is to focus on subt...

Guolei Sun, et al. ∙

research

∙ 08/23/2020

Learning Dynamical Systems using Local Stability Priors

A coupled computational approach to simultaneously learn a vector field ...

Arash Mehrjou, et al. ∙

research

∙ 10/01/2020

Few-Shot Classification By Few-Iteration Meta-Learning

Learning in a low-data regime from only a few labeled examples is an imp...

Ardhendu Shekhar Tripathi, et al. ∙

research

∙ 05/17/2021

Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report

Video super-resolution has recently become one of the most important mob...

Andrey Ignatov, et al. ∙

research

∙ 07/09/2020

Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE

Probabilistic modelling has been an essential tool in medical image anal...

Anna Volokitin, et al. ∙

research

∙ 06/14/2019

(1 + ε)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets

Anomaly detection is not an easy problem since distribution of anomalous...

Maxim Borisyak, et al. ∙

research

∙ 09/18/2019

No-Regret Learning in Unknown Games with Correlated Payoffs

We consider the problem of learning to play a repeated multi-agent game ...

Pier Giuseppe Sessa, et al. ∙

research

∙ 06/16/2022

SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation

Adapting to a continuously evolving environment is a safety-critical cha...

Tao Sun, et al. ∙

research

∙ 07/17/2020

Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search

In this paper, we introduce a new reinforcement learning (RL) based neur...

Yuan Tian, et al. ∙

research

∙ 01/06/2019

RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials

In this paper, we consider the problem of reconstructing a dense 3D mode...

Despoina Paschalidou, et al. ∙

research

∙ 10/10/2019

An Anatomy of Graph Neural Networks Going Deep via the Lens of Mutual Information: Exponential Decay vs. Full Preservation

Graph Convolutional Network (GCN) has attracted intensive interests rece...

Nezihe Merve Gürel, et al. ∙

research

∙ 07/21/2021

HistoCartography: A Toolkit for Graph Analytics in Digital Pathology

Advances in entity-graph based analysis of histopathology images have br...

Guillaume Jaume, et al. ∙

research

∙ 04/09/2020

DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution

Super-resolution (SR) is by definition ill-posed. There are infinitely m...

Marcel Christoph Bühler, et al. ∙

research

∙ 01/10/2019

No-regret Bayesian Optimization with Unknown Hyperparameters

Bayesian optimization (BO) based on Gaussian process models is a powerfu...

Felix Berkenkamp, et al. ∙

research

∙ 12/22/2019

Active Learning for Segmentation Based on Bayesian Sample Queries

Segmentation of anatomical structures is a fundamental image analysis ta...

Firat Ozdemir, et al. ∙

research

∙ 09/24/2021

Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning

In this work, we present and study a training set-up that achieves fast ...

Nikita Rudin, et al. ∙

research

∙ 02/01/2022

Continual Attentive Fusion for Incremental Learning in Semantic Segmentation

Over the past years, semantic segmentation, as many other tasks in compu...

Guanglei Yang, et al. ∙

research

∙ 05/08/2020

NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

This paper reviews the NTIRE 2020 challenge on real image denoising with...

Abdelrahman Abdelhamed, et al. ∙

research

∙ 03/20/2022

Transform your Smartphone into a DSLR Camera: Learning the ISP in the Wild

We propose a trainable Image Signal Processing (ISP) framework that prod...

Ardhendu Shekhar Tripathi, et al. ∙

research

∙ 10/11/2019

Improving Gradient Estimation in Evolutionary Strategies With Past Descent Directions

Evolutionary Strategies (ES) are known to be an effective black-box opti...

Florian Meier, et al. ∙

research

∙ 10/19/2021

Multi-Objective Loss Balancing for Physics-Informed Deep Learning

Physics Informed Neural Networks (PINN) are algorithms from deep learnin...

Rafael Bischof, et al. ∙

research

∙ 03/04/2020

Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement

The recent years have witnessed the great potential of deep learning for...

Ren Yang, et al. ∙

research

∙ 03/18/2019

EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras

We present the first event-based learning approach for motion segmentati...

Anton Mitrokhin, et al. ∙

research

∙ 03/12/2020

AirSim Drone Racing Lab

Autonomous drone racing is a challenging research problem at the interse...

Ratnesh Madaan, et al. ∙

research

∙ 11/15/2021

Interpretability Aware Model Training to Improve Robustness against Out-of-Distribution Magnetic Resonance Images in Alzheimer's Disease Classification

Owing to its pristine soft-tissue contrast and high resolution, structur...

Merel Kuijs, et al. ∙

research

∙ 06/24/2020

Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model

The past few years have witnessed increasing interests in applying deep ...

Ren Yang, et al. ∙

research

∙ 04/04/2022

SHiFT: An Efficient, Flexible Search Engine for Transfer Learning

Transfer learning can be seen as a data- and compute-efficient alternati...

Cedric Renggli, et al. ∙

research

∙ 05/31/2022

k-Means Maximum Entropy Exploration

Exploration in high-dimensional, continuous spaces with sparse rewards i...

Alexander Nedergaard, et al. ∙

research

∙ 09/02/2019

This is not what I imagined: Error Detection for Semantic Segmentation through Visual Dissimilarity

There has been a remarkable progress in the accuracy of semantic segment...

David Haldimann, et al. ∙

research

∙ 10/09/2019

Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-Resolution

In single image super-resolution (SISR), given a low-resolution (LR) ima...

Xin Deng, et al. ∙

research

∙ 12/26/2019

Efficient Video Semantic Segmentation with Labels Propagation and Refinement

This paper tackles the problem of real-time semantic segmentation of hig...

Matthieu Paul, et al. ∙

research

∙ 06/22/2020

Continual Learning in Recurrent Neural Networks with Hypernetworks

The last decade has seen a surge of interest in continual learning (CL),...

Benjamin Ehret, et al. ∙

research

∙ 02/06/2021

COVIDHunter: An Accurate, Flexible, and Environment-Aware Open-Source COVID-19 Outbreak Simulation Model

Motivation: Early detection and isolation of COVID-19 patients are essen...

Mohammed Alser, et al. ∙

research

∙ 04/06/2021

Variational Transformer Networks for Layout Generation

Generative models able to synthesize layouts of different kinds (e.g. do...

Diego Martin Arroyo, et al. ∙

research

∙ 08/21/2021

3D Reconstruction from public webcams

In this paper, we investigate the possibility of reconstructing the 3D g...

Tianyu Wu, et al. ∙

research

∙ 11/29/2021

DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

As acquiring pixel-wise annotations of real-world images for semantic se...

Lukas Hoyer, et al. ∙

research

∙ 03/24/2022

Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devices

Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devi...

Alessio Burrello, et al. ∙

research

∙ 06/14/2022

Neural interval-censored Cox regression with feature selection

The classical Cox model emerged in 1972 promoting breakthroughs in how p...

Carlos García Meixide, et al. ∙

research

∙ 11/19/2019

Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scene

This work addresses the task of dense 3D reconstruction of a complex dyn...

Suryansh Kumar, et al. ∙

research

∙ 07/04/2020

Self-Calibration Supported Robust Projective Structure-from-Motion

Typical Structure-from-Motion (SfM) pipelines rely on finding correspond...

Rui Gong, et al. ∙

research

∙ 12/18/2018

Information-Directed Exploration for Deep Reinforcement Learning

Efficient exploration remains a major challenge for reinforcement learni...

Nikolay Nikolov, et al. ∙

research

∙ 01/06/2020

Chained Representation Cycling: Learning to Estimate 3D Human Pose and Shape by Cycling Between Representations

The goal of many computer vision systems is to transform image pixels in...

Nadine Rueegg, et al. ∙

research

∙ 01/29/2020

A scale-dependent notion of effective dimension

We introduce a notion of "effective dimension" of a statistical model ba...

Oksana Berezniuk, et al. ∙

research

∙ 02/03/2020

Efficient 2D neuron boundary segmentation with local topological constraints

We present a method for segmenting neuron membranes in 2D electron micro...

Thanuja D. Ambegoda, et al. ∙

ETH Zurich

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