Stars
My personal notes on how to use MLflow, compiled after following courses & tutorials, and after making personal experiences.
A pytorch implementation of Maximum Mean Discrepancies(MMD) loss
An implementation of Maximum Mean Discrepancy (MMD) as a differentiable loss in PyTorch.
Pytorch implementation of Conditional-GAN (CGAN)
Implementation of a Wasserstein Generative Adversarial Network with Gradient Penalty to enforce lipchitz constraint. The WGAN utilizes the wasserstein loss or critic as its loss function instead of…
code for "Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning"
Multi-level Consistency Learning for Semi-supervised Domain Adaptation, IJCAI 2022
This is a PyTorch implementation of the Unsupervised Domain Adaptation method proposed in the paper Deep CORAL: Correlation Alignment for Deep Domain Adaptation. Baochen Sun and Kate Saenko (ECCV 2…
PyTorch implementations of Generative Adversarial Networks.
Semi-supervised Domain Adaptation via Minimax Entropy
Semi-Supervised Domain Adaptation with Source Label Adaptation, accepted to CVPR 2023
SpotTune: Transfer Learning through Adaptive Fine-tuning
Tools to Design or Visualize Architecture of Neural Network
Implementation of the paper "CXR-IRGen: An Integrated Vision and Language Model for the Generation of Clinically Accurate Chest X-Ray Image-Report Pairs" (WACV 2024)
Starter files, final projects, and FAQ for my Complete JavaScript course