tp030ny

3d-lmnet

Repository for 3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image [BMVC 2018]

3D-Machine-Learning

A resource repository for 3D machine learning

3D_generation

AA-RMVSNet

Code for AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network (ICCV2021).

AI-News

Summarize the paper and code in AI(Semantic Segmentation, Medical Segmentation,REID,Super-Resolution,Registration,CVPR,ECCV,ICCV,AAAI,MICCAI)

ANN2SNN_SRP

ANN2SNNConversion_SNM_NeuronNorm

Pytorch Implementation of Signed Neuron with Memory: Towards Simple, Accurate and High-Efficient ANN-SNN Conversion, IJCAI 2022

IF_toolbox

Fit stochastic IF model with spike-triggered current eta and moving threshold gamma as described in: "Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms"

NEUROFIT

NEUROFIT is a program that fits Hodgkin-Huxley models to voltage-clamp data.

positional_cl

code for paper Positional Contrastive Learning for Volumetric Medical Image Segmentation

tp030ny/positional_cl

code for paper Positional Contrastive Learning for Volumetric Medical Image Segmentation

tp030ny/ANN2SNN_SRP

tp030ny/ANN2SNNConversion_SNM_NeuronNorm

Pytorch Implementation of Signed Neuron with Memory: Towards Simple, Accurate and High-Efficient ANN-SNN Conversion, IJCAI 2022

tp030ny/Awesome-Diffusion-Models-in-Medical-Imaging

Diffusion Models in Medical Imaging

tp030ny/awesome-spiking-neural-networks

A curated list of materials for Spiking Neural Networks, 3rd generation of artificial neural networks.

tp030ny/bindsnet

Simulation of spiking neural networks (SNNs) using PyTorch.

tp030ny/brian2

Brian is a free, open source simulator for spiking neural networks.

tp030ny/brian2modelfitting

Model fitting toolbox for the Brian 2 simulator

tp030ny/CLIP

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

tp030ny/direct-training-snn

This project used STBP-tdBN method to directly train Deep Spiking Neural Networks from scratch with PyTorch

tp030ny/DSR

[CVPR 2022] Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation

tp030ny/esm

Evolutionary Scale Modeling (esm): Pretrained language models for proteins

tp030ny/gpt_academic

为ChatGPT/GLM提供实用化交互界面，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm2等本地模型。兼容文心一言, moss, llama2, rwkv, claude2, 通义千问, 书生, 讯飞星火等。

tp030ny/OptSNNConvertion-RNL-RIL

tp030ny/simCLR-snn

PyTorch implementation of SimCLR by snn: A Simple Framework for Contrastive Learning of Visual Representations

tp030ny/SNN-event-driven-learning

An event-driven learning algorithm for spiking neural networks

tp030ny/SNN_conversion_QCFS

tp030ny/SNN_CV_Applications_Resources

Paper list for SNN based computer vision tasks.

tp030ny/snn_toolbox

Toolbox for converting analog to spiking neural networks (ANN to SNN), and running them in a spiking neuron simulator.

tp030ny/snntorch

Deep and online learning with spiking neural networks in Python

tp030ny/Spike-Element-Wise-ResNet

Deep Residual Learning in Spiking Neural Networks

tp030ny/Spiking-Neural-Network-SNN-with-PyTorch-where-Backpropagation-engenders-STDP

What about coding a Spiking Neural Network using an automatic differentiation framework? In SNNs, there is a time axis and the neural network sees data throughout time, and activation functions are instead spikes that are raised past a certain pre-activation threshold. Pre-activation values constantly fades if neurons aren't excited enough.

tp030ny/spikingjelly

SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.

tp030ny/SSL-MedSeg

Official implementation of our paper "Self-Supervised Pretraining for 2D Medical Image Segmentation"

tp030ny/SSL_medicalimaging

Codebase for Imperial MSc AI Group Project: How Well Do Self-Supervised Models Transfer to Medical Imaging?

tp030ny/StableGNN

StableGNN-Generalizing Graph Neural Networks on Out-Of-Distribution Graphs

tp030ny/STBP-simple

A simple direct training implement for SNNs using Spatio-Temporal Backpropagation

tp030ny/STDP-based-DCNN

Reimplementation of the paper "STDP-based spiking deep convolutional neural networks for object recognition"

tp030ny/Supervised-SNN-with-GD

A supervised learning algorithm of SNN is proposed by using spike sequences with complex spatio-temporal information. We explore an error back-propagation method of SNN based on gradient descent. The chain rule proved mathematically that it is sufficient to update the SNN’s synaptic weights by directly using an optimizer. Utilizing the TensorFlow framework, a bilayer supervised learning SNN is constructed from scratch. We take the lead in the application of SAR image classification and conduct experiments on the MSTAR dataset.

tp030ny/temporal_efficient_training

Code for temporal efficient training