bcai
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boschresearch / unetgan
Official Implementation of the paper "A U-Net Based Discriminator for Generative Adversarial Networks" (CVPR 2020)
Language:Python 364Official implementation of the paper "You Only Need Adversarial Supervision for Semantic Image Synthesis" (ICLR 2021)
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Source code for Fathony, Sahu, Willmott, & Kolter, "Multiplicative Filter Networks", ICLR 2021.
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Implementation of the paper "Understanding anomaly detection with deep invertible networks through hierarchies of distributions and features" (NeurIPS 2020)
Language:Python 42Code accompanying Coling2020 publication on data augmentation for named entity recognition
Language:Python 33Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
Language:Python 31Code base for physics-based photorealistic rendering within the scope of Bosch BCAI AMIRA probject
Language:Python 27Blackbox optimization algorithms with a common interface, along with useful helpers like parallel optimization loops, analysis and visualization scripts.
Language:Python 24Tensorflow implementation of Meta Adversarial Training for Adversarial Patch Attacks on Tiny ImageNet.
Language:Python 23Resources related to ACL 2020 paper "The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain"
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A toolbox to explore synchronous layerwise-parallel deep neural networks.
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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.
Language:Python 14Resources related to EMNLP 2021 paper "FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations"
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This is the companion code for the method reported in the paper "Learning game-theoretic models of multiagent trajectories using implicit layers" published at AAAI 2021
Language:Python 12Implementation prototype of the Deep Deterministic Off-Policy Gradient (DD-OPG) method.
Language:Python 11Code for toy dataset generation of "Grid Saliency for Context Explanations of Semantic Segmentation" (https://arxiv.org/abs/1907.13054)
Language:Python 11Official Implementation of "Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning".
Language:Python 11This is the companion code for the paper Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization by Lukas P. Fröhlich et al., AISTATS 2020
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Official implementation of the ICLR 2021 paper "Differentiable Trust Region Layers for Deep Reinforcement Learning"
Language:Python 9Companion code for the paper "Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks" by Yatsura et al.
Language:Python 8Source code for Sheikholeslami et al., "Provably Robust Classification of Adversarial Examples with Detection", ICLR 2021.
Language:Python 8Define parameter spaces of mixed types like continuous, integers and categoricals along with conditionalities and priors.
Language:Python 7Source code for Bechtold et al., "Fostering Generalization in Single-view 3D Reconstruction by Learning a Hierarchy of Local and Global Shape Priors", CVPR 2021.
Language:Python 6Code accompanying the ICLR 2021 paper "ResNet After All? Neural ODEs and Their Numerical Solution"
Language:Python 6Code for "Progressive Augmentation of GANs" (https://arxiv.org/abs/1901.10422)
Language:Python 6Dataset of a collection of commonsense knowledge assertions relevant for the autonomous driving (AD) domain.
Source code for Volpp et al., "Bayesian Context Aggregation for Neural Processes", ICLR 2021
Language:Python 5ClusterDataSplit: Exploring Challenging Clustering-Based Data Splits for Model Performance Evaluation
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