Fei Ye's starred repositories
Awesome-diffusion-model-for-image-processing
one summary of diffusion-based image processing, including restoration, enhancement, coding, quality assessment
awesome-diffusion-categorized
collection of diffusion model papers categorized by their subareas
Bayesian_neural_network_papers
Papers for Bayesian-NN
awesome-latex-drawing
Drawing Bayesian networks, graphical models, tensors, technical frameworks, and illustrations in LaTeX.
Single-Image-Super-Resolution
A collection of high-impact and state-of-the-art SR methods
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, ... 🧠
PyTorch-GAN
PyTorch implementations of Generative Adversarial Networks.
the-gan-zoo
A list of all named GANs!
Truncated-Cauchy-Non-Negative-Matrix-Factorization
Non-negative matrix factorization (NMF) minimizes the euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers. In this paper, we propose a Truncated CauchyNMF loss that handle outliers by truncating large errors, and develop a Truncated CauchyNMF to robustly learn the subspace on noisy datasets contaminated by outliers. We theoretically analyze the robustness of Truncated CauchyNMF comparing with the competing models and theoretically prove that Truncated CauchyNMF has a generalization bound which converges at a rate of order O(lnn/n‾‾‾‾‾√) , where n is the sample size. We evaluate Truncated CauchyNMF by image clustering on both simulated and real datasets. The experimental results on the datasets containing gross corruptions validate the effectiveness and robustness of Truncated CauchyNMF for learning robust subspaces.
Free-shared-Spatiotemporal-method-of-remote-sensing-
spatiotemporal fusion method
ComputerVision
研究生的科研记录，目前研究方向是计算机视觉。
FuVarRelease
codes for the paper "Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability", IEEE TIP 2020
google-access-helper
谷歌访问助手破解版、谷歌翻墙、谷歌梯子、谷歌梯子扩展工具、谷歌商店访问、Chrome翻墙
unmixing_spectral_variability
A complete toolbox for spectral unmixing with spectral variability
sci-hub-now
Free access to academic papers with just a single click! Abolish publishers, long live the open access movement! 🦅