Official Implementation of the paper "A U-Net Based Discriminator for Generative Adversarial Networks" (CVPR 2020)
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Updated
Apr 14, 2022 - Python
Official Implementation of the paper "A U-Net Based Discriminator for Generative Adversarial Networks" (CVPR 2020)
Source code for Fathony, Sahu, Willmott, & Kolter, "Multiplicative Filter Networks", ICLR 2021.
[DEPRECATED] Procedural generation library for Gazebo (please refer to https://github.com/boschresearch/pcg_gazebo)
Implementation of the paper "Understanding anomaly detection with deep invertible networks through hierarchies of distributions and features" (NeurIPS 2020)
Coder of the paper 'Latent Outlier Exposure for Anomaly Detectin with Contaminated Data' published in ICML 2022
Code of the paper 'Neural Transformation Learning for Anomaly Detection' published in ICML 2021
Code accompanying Coling2020 publication on data augmentation for named entity recognition
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
Code base for physics-based photorealistic rendering within the scope of Bosch BCAI AMIRA probject
Supplementary source code for the ECRTS 2019 paper 'Response-Time Analysis of ROS 2 Processing Chains under Reservation-Based Scheduling'
Tensorflow implementation of Meta Adversarial Training for Adversarial Patch Attacks on Tiny ImageNet.
Code of the paper 'Raising the Bar in Graph-level Anomaly Detection' published in IJCAI-2022
[CVPR 2022] What Matters For Meta-Learning Vision Regression Tasks?
Simultaneous task allocation and motion scheduling (STAAMS) solver based on constraint programming and optimization, implemented for the Robot Operating System (ROS)
Meta-Learning of Neural Architectures for Few-Shot Learning
Companion code for the self-supervised anomaly detection algorithm proposed in the paper "Detecting Anomalies within Time Series using Local Neural Transformations" by Tim Schneider et al.
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