Christopher Zach
Christopher Zach is a research professor in the research group Computer vision and medical image analysis. His main research interests are 3D reconstruction from images and 3D image understanding. Further, he conducts research in efficient and real-time methods for computer vision and machine learning, and therefore he also explores underlying mathematical models and suitable numerical optimization methods.
Showing 29 publications
When IC meets text: Towards a rich annotated integrated circuit text dataset
Deep Nearest Neighbors for Anomaly Detection in Chest X-Rays
Decentralized Training of 3D Lane Detection with Automatic Labeling Using HD Maps,
Deep-learning-based out-of-distribution data detection in visual inspection images
Fully Variational Noise-Contrastive Estimation
Cycle-Object Consistency for Image-to-Image Domain Adaptation
Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons
GEN: Pushing the Limits of Softmax-Based Out-of-Distribution Detection
Extremely Low-light Image Enhancement with Scene Text Restoration
CyEDA : CYCLE OBJECT EDGE CONSISTENCY DOMAIN ADAPTATION
Industrial X-ray Image Analysis with Deep Neural Networks Robust to Unexpected Input Data
AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks
Data Augmentation via Neural-Style-Transfer for Driver Distraction Recognition
Effortless Training of Joint Energy-Based Models with Sliced Score Matching
Joint Energy-based Model for Deep Probabilistic Regression
Robust Fitting with Truncated Least Squares: A Bilevel Optimization Approach
Analysis of industrial X-ray computed tomography data with deep neural networks
AUTOENCODER-BASED ANOMALY DETECTION IN INDUSTRIAL X-RAY IMAGES
Progressive Batching for Efficient Non-linear Least Squares
BabelCalib: A Universal Approach to Calibrating Central Cameras
Contrastive learning for lifted networks
Lifted Regression/Reconstruction Networks
A Graduated Filter Method for Large Scale Robust Estimation
SG-VAE: Scene Grammar Variational Autoencoder to Generate New Indoor Scenes
Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions
Pareto meets huber: Efficiently avoiding poor minima in robust estimation
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Showing 4 research projects
Learning and Leveraging Rich Priors for Factorization Problems
Energy-based models for supervised deep neural networks and their applications
AI for Analysis for Naturalistic Driving Data
Automatic data analysis within non-destructive evaluation (ADA-NDE)