brandonywl

brandonywl's Stars

• SHI-Labs/Diffusion-Driven-Test-Time-Adaptation-via-Synthetic-Domain-Alignment

  Everything to the Synthetic: Diffusion-driven Test-time Adaptation via Synthetic-Domain Alignment

  Language:Python222

• mariodoebler/test-time-adaptation

  A repository and benchmark for online test-time adaptation.

  Language:Python20022

• prs-eth/Marigold

  [CVPR 2024 - Oral, Best Paper Award Candidate] Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation

  Language:Python2.5k144

• shiyegao/DDA

  Official repository of "Back to Source: Diffusion-Driven Test-Time Adaptation"

  Language:Python748

• zyxElsa/InST

  Official implementation of the paper “Inversion-Based Style Transfer with Diffusion Models” (CVPR 2023)

  Language:Jupyter Notebook54550

• facebookresearch/disentangling-correlated-factors

  A benchmarking suite for disentanglement algorithms, suited for evaluating robustness to correlated factors. Codebase for the paper "Disentanglement of Correlated Factors via Hausdorff Factorized Support" by Karsten Roth, Mark Ibrahim, Zeynep Akata, Pascal Vincent, Diane Bouchacourt.

  Language:Python719

• google/style-aligned

  Official code for "Style Aligned Image Generation via Shared Attention"

  Language:Python1.2k92

• tim-learn/awesome-test-time-adaptation

  Collection of awesome test-time (domain/batch/instance) adaptation methods

  81256

• qinenergy/cotta

  [CVPR 2022] Official CoTTA Code for our paper Continual Test-Time Domain Adaptation

  Language:Python25827

• jindongwang/transferlearning

  Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

  Language:Python13.6k3.8k

• mvp18/TTT-UCDR

  [https://arxiv.org/abs/2208.09198] Test-Time Training for Universal Cross-Domain Retrieval

  Language:Python8

• fmu2/flow-VAE

  Variational Autoencoder (VAE) with Normalizing Flows

  Language:Python416

• tatp22/multidim-positional-encoding

  An implementation of 1D, 2D, and 3D positional encoding in Pytorch and TensorFlow

  Language:Python55835

• MineDojo/MineDojo

  Building Open-Ended Embodied Agents with Internet-Scale Knowledge

  Language:Java1.9k165

• Yetiowner/Increasing-code-complexity

  Every day, I commit a new and more complicated version of some simple code.

  Language:Python2246

• TsingZ0/PFLlib

  37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 24 datasets. www.pfllib.com/

  Language:Python1.6k320

• jeremy313/non-iid-dataset-for-personalized-federated-learning

  Official implementation of "FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective".

  Language:Python8323

• chuahou/mctele

  Telegram bot that notifies when player joins Minecraft server

  Language:Haskell4

• drivendataorg/cookiecutter-data-science

  A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

  Language:Python8.5k2.5k

• r45635/HVAC-IR-Control

  Ir Send - Updated for HVAC Mitsubishi & Panasonic IR

  Language:C++25389

• mar-wir/dict_tools_godot

  Tools for accessing (reading/writing) data stored inside nested dictionaries in Godot. Also converts to CSV.

  Language:GDScript42

• zhiayang/mikoto

  Bluetooth LE nRF52840 microcontroller in a pro-micro footprint.

  Language:HTML28949

• Borodutch/telegram-bot-starter

  Telegram bot starter based on grammY

  Language:TypeScript23651

• alpv95/Dank-Learning

  Dank Learning codebase, generate a meme from any image using AI. Uses a modified version of the Show and Tell image captioning network

  Language:Jupyter Notebook48380

• awesomedata/awesome-public-datasets

  A topic-centric list of HQ open datasets.

  61.6k10k

• amueller/ml-workshop-4-of-4

  Advanced Machine Learning with Scikit-learn part II

  Language:HTML162104

• amueller/ml-workshop-3-of-4

  Advanced Machine Learning with Scikit-learn part I

  Language:HTML140120

• amueller/ml-workshop-2-of-4

  Intermediate Machine Learning with Scikit-learn, 4h interactive workshop

  Language:HTML126115

• amueller/ml-workshop-1-of-4

  Introduction to Machine learning with Python, 4h interactive workshop

  Language:HTML305156

• amueller/introduction_to_ml_with_python

  Notebooks and code for the book "Introduction to Machine Learning with Python"

  Language:Jupyter Notebook7.5k4.6k