ludandandan's repositories

APDrawingGAN2

Code for Line Drawings for Face Portraits from Photos using Global and Local Structure based GANs (TPAMI)

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annotated_deep_learning_paper_implementations

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

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APDrawingGAN

Code for APDrawingGAN: Generating Artistic Portrait Drawings from Face Photos with Hierarchical GANs (CVPR 2019 Oral)

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ChatGLM2-6B

ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型

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cvxbook_additional_exercises

Additional exercises and data for EE364a. No solutions; for public consumption.

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ddim

Denoising Diffusion Implicit Models

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deep-learning-from-scratch

『ゼロから作る Deep Learning』(O'Reilly Japan, 2016)

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examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

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Face-Sketch-Wild

Semi-Supervised Learning for Face Sketch Synthesis in the Wild, ACCV2018

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FaceSketch-Awesome-List

Deep Facial Synthesis: A New Challenge

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HIDA

[ICCV'23] Human-Inspired Facial Sketch Synthesis with Dynamic Adaptation

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improved-diffusion

Release for Improved Denoising Diffusion Probabilistic Models

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Kaggel_Course

Kaggel_Course

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MS-SSIM_L1_LOSS

Pytorch implementation of MS-SSIM L1 Loss function

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nanoGPT

The simplest, fastest repository for training/finetuning medium-sized GPTs.

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Palette-Image-to-Image-Diffusion-Models

Unofficial implementation of Palette: Image-to-Image Diffusion Models by Pytorch

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PerceptualSimilarity

LPIPS metric. pip install lpips

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PhotoSketchMAN

Code for paper "High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks"

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pix2pixHD

Synthesizing and manipulating 2048x1024 images with conditional GANs

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pytorch-fid

Compute FID scores with PyTorch.

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PyTorch-VAE

A Collection of Variational Autoencoders (VAE) in PyTorch.

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SPIGA

SPIGA: Shape Preserving Facial Landmarks with Graph Attention Networks.

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