Torch MinGRU implementation based on "Were RNNs All We Needed?"

parallel LSTM from paper Were RNNs All We Needed?.

Official implementation of TinyCD: A (Not So) Deep Learning Model For Change Detection

[NeurIPS 2024] Official implementation of the paper "Are Self-Attentions Effective for Time Series Forecasting?"

Implementation of the proposed minGRU in Pytorch

DunHuangStitch: Unsupervised Deep Image Stitching of Dunhuang Murals

Mural image inpainting, Mural damage repair, Line drawing guided image inpainting

MATLAB implementation for Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k-Means Clustering.

Code for TGRS paper "UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation"

Count the MACs / FLOPs of your PyTorch model.

SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation

Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.

User-friendly AI Interface (Supports Ollama, OpenAI API, ...)

(Realtime) Temporal Convolutions in PyTorch

Get up and running with Llama 3.3, Mistral, Gemma 2, and other large language models.

List of datasets, codes, and contests related to remote sensing change detection

A review of change detection methods, including codes and open data sets for deep learning. From paper: change detection based on artificial intelligence: state-of-the-art and challenges.

A framework for change detection using PyTorch

Effortless data labeling with AI support from Segment Anything and other awesome models.

The repository provides code implementations and illustrative examples of NeurIPS 2023 paper, Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning.

[ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"

About Code release for "SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling" (NeurIPS 2023 Spotlight), https://arxiv.org/abs/2302.00861

Self-supervised contrastive learning for time series via time-frequency consistency

[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"

Awesome Deep Learning for Time-Series Imputation, including a must-read paper list about applying neural networks to impute incomplete time series containing NaN missing values/data

Time Aware LSTM Cell implementation in Pytorch

Graph Neural Networks for Irregular Time Series