patrickk00

patrickk00's Stars

• ZongxianLee/MMD_Loss.Pytorch

  A pytorch implementation of Maximum Mean Discrepancies(MMD) loss

  Language:Python19568

• yiftachbeer/mmd_loss_pytorch

  An implementation of Maximum Mean Discrepancy (MMD) as a differentiable loss in PyTorch.

  Language:Python242

• youssefHosni/Data-Science-Interview-Questions-Answers

  Curated list of data science interview questions and answers

  3.2k742

• qbxlvnf11/conditional-GAN

  Pytorch implementation of Conditional-GAN (CGAN)

  Language:Jupyter Notebook397

• u7javed/Conditional-WGAN-GP

  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 the vanilla GAN loss. It has shown to perform better as is often used as a solution to mode collapse, a common issue in GANs where the generator produces limited variation after finding a way to trick the discriminator into always losing. This cycle forces the generator to not produce any new variation. The Wasserstein loss is also known as the Earth Mover Distance

  Language:Jupyter Notebook134

• Sakura03/SemiMeta

  Language:Python134

• bupt-ai-cz/ProML

  code for "Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning"

  Language:Python151

• chester256/MCL

  Multi-level Consistency Learning for Semi-supervised Domain Adaptation, IJCAI 2022

  Language:Python151

• DenisDsh/PyTorch-Deep-CORAL

  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 2016).

  Language:Python4918

• eriklindernoren/PyTorch-GAN

  PyTorch implementations of Generative Adversarial Networks.

  Language:Python16.3k4.1k

• VisionLearningGroup/SSDA_MME

  Semi-supervised Domain Adaptation via Minimax Entropy

  Language:Python29946

• chu0802/SLA

  Semi-Supervised Domain Adaptation with Source Label Adaptation, accepted to CVPR 2023

  Language:Python369

• gyhui14/spottune

  SpotTune: Transfer Learning through Adaptive Fine-tuning

  Language:Python8823

• ashishpatel26/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network

  Tools to Design or Visualize Architecture of Neural Network

  4.4k574

• junjie-shentu/CXR-IRGen

  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)

  Language:Python121

• lllyasviel/Fooocus

  Focus on prompting and generating

  Language:Python40.6k5.7k

• patrickk00/Exploring-Milan-s-restaurant-scene

  Language:Jupyter Notebook1

• patrickk00/complete-javascript-course

  Starter files, final projects, and FAQ for my Complete JavaScript course

  1

• aelassas/bookcars

  Car Rental Platform with Mobile App

  Language:TypeScript305195

• cheshire-cat-ai/core

  Production ready AI agent framework

  Language:Python2.3k311

• sagunms/ComputerVisionProjects

  Computer Vision Projects

  Language:C++158

• IBM/sifar-pytorch

  super image for action recognition

  Language:Python559

• amazon-science/object-centric-multiple-object-tracking

  Language:Python353