jamekuma

Server_Manager

Notes about server management

ai-deadlines

:alarm_clock: AI conference deadline countdowns

APSNet

CIPS

Official repository for the paper "Image Generators with Conditionally-Independent Pixel Synthesis" (CVPR2021, Oral)

ClassSR

(CVPR2021) ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

EDSR-PyTorch

PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

few-shot-meta-baseline

Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning, in ICCV 2021

FinGAT

IGNN

Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" (NeurIPS'20)

inr-gan

Adversarial Generation of Continuous Images [CVPR 2021]

Invertible-Image-Rescaling

Implementation of paper: Invertible Image Rescaling

jamekuma.github.io

My Blog

learning3d

This is a complete package of recent deep learning methods for 3D point clouds in pytorch (with pretrained models).

liif

Learning Continuous Image Representation with Local Implicit Image Function, in CVPR 2021 (Oral)

MetaSampler

[ECCV 2022] Meta-Sampler: Almost Universal yet Task-Oriented Sampling for Point Clouds

MyComplier

哈工大2020年春季学期编译系统实验

myINNSR

本科毕设

Note

openpoints

OpenPoints: a library for easily reproducing point-based methods for point cloud understanding. The engine for [PointNeXt](https://arxiv.org/abs/2206.04670)

pointMLP-pytorch

[ICLR 2022 poster] Official PyTorch implementation of "Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework"

Pointnet_Pointnet2_pytorch

PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.

PointNeXt

[NeurIPS'22] PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies

Prototypical-Networks-for-Few-shot-Learning-PyTorch

Implementation of Prototypical Networks for Few Shot Learning (https://arxiv.org/abs/1703.05175) in Pytorch

pytorch_wavelets

Pytorch implementation of 2D Discrete Wavelet (DWT) and Dual Tree Complex Wavelet Transforms (DTCWT) and a DTCWT based ScatterNet

qlib

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

SampleNet

Differentiable Point Cloud Sampling (CVPR 2020 Oral)

second.pytorch

SECOND for KITTI/NuScenes object detection

siren

Official implementation of "Implicit Neural Representations with Periodic Activation Functions"

SpareNet

Style-based Point Generator with Adversarial Rendering for Point Cloud Completion (CVPR 2021)

Temporal_Relational_Stock_Ranking