GeJulia
Ph.D. in Computer Vision at Mannheim University in cooperation with Fraunhofer ITWM, Kaiserslautern
Germany
GeJulia's Stars
paulgavrikov/visualkeras
Visualkeras is a Python package to help visualize Keras (either standalone or included in TensorFlow) neural network architectures. It allows easy styling to fit most needs. This module supports layered style architecture generation which is great for CNNs (Convolutional Neural Networks), and a graph style architecture, which works great for most models including plain feed-forward networks.
YuhangSong/Prospective-Configuration
This repository contains the code for the paper "Inferring Neural Activity Before Plasticity: A Foundation for Learning Beyond Backpropagation."
infer-actively/pypc
Predictive coding in Python
B-cos/B-cos-v2
Official PyTorch implementation of improved B-cos models
paulgavrikov/CNN-Filter-DB
A database of over 1.4 billion 3x3 convolution filters extracted from hundreds of diverse CNN models with relevant meta information (CVPR 2022 ORAL)
tym002/Hyper-Convolution
Hyper-Convolution Networks for Biomedical Image Segmentation
steffen-jung/SpectralGAN
Code accompanying the AAAI 2021 paper "Spectral Distribution Aware Image Generation".
steffen-jung/robustness-dataset
GitHub repository of the ICLR 2023 paper "Neural Architecture Design and Robustness: A Dataset"
jovitalukasik/AG-Net
Code for "Learning Where To Look – Generative NAS is Surprisingly Efficient"
jovitalukasik/GNN_predictor
Neural Architecture Performance PredictionUsing Graph Neural Networks
paulgavrikov/cvpr22w_RobustnessThroughTheLens
Official repository of our submission "Adversarial Robustness through the Lens of Convolutional Filters" for the CVPR2022 Workshop "The Art of Robustness: Devil and Angel in Adversarial Machine Learning Workshop"
steffen-jung/FID-bias
Code accompanying the NeurIPS 2021 Workshop on Distribution Shifts paper "Internalized Biases in Fréchet Inception Distance".
steffen-jung/GCN-Multicut
paulgavrikov/tinytorchtrainer
A small framework to train image classifiers in PyTorch