Christopher Zach
Christopher Zach är forskningsprofessor i forskargruppen Digitala bildsystem och bildanalys.
Visar 29 publikationer
When IC meets text: Towards a rich annotated integrated circuit text dataset
Deep Nearest Neighbors for Anomaly Detection in Chest X-Rays
Fully Variational Noise-Contrastive Estimation
Cycle-Object Consistency for Image-to-Image Domain Adaptation
Deep-learning-based out-of-distribution data detection in visual inspection images
Decentralized Training of 3D Lane Detection with Automatic Labeling Using HD Maps,
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
Effortless Training of Joint Energy-Based Models with Sliced Score Matching
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
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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Visar 4 forskningsprojekt
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)