Popular GitHub Repositories for Unlabeled Mapf

SoundNet: Learning Sound Representations from Unlabeled Video. NIPS 2016

ONNX-compatible Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data

Powerful unsupervised domain adaptation method for dense retrieval. Requires only unlabeled corpus and yields massive improvement: "GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval" https://arxiv.org/abs/2112.07577

ICCV2017 Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro

ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation (CVPR20)

POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF settings.

This is a multi-agent path planning(also known as Multi-Agent Path Finding, MAPF) algorithm package for ROS

Pipeline for particle picking in cryo-electron microscopy images using convolutional neural networks trained from positive and unlabeled examples. Also featuring micrograph and tomogram denoising with DNNs.

Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus.

A Pytorch Implementation of "Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro"(ICCV17)

DDFlow: Learning Optical Flow with Unlabeled Data Distillation

Algorithm for prioritized multi-agent path finding (MAPF) in grid-worlds. Moves into arbitrary directions are allowed (each agent is allowed to follow any-angle path on the grid). Timeline is continuous, i.e. action durations are not explicitly discretized into timesteps. Different agents' size and moving speed are supported. Planning is carried out in (x, y, \theta) configuration space, i.e. agents' orientation are taken into account.

Exploiting unlabeled data with vision and language models for object detection, ECCV 2022

Code release for "Learning to Detect Mobile Objects from LiDAR Scans Without Labels" [CVPR 2022]

Engineering LaCAM*: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding (AAMAS-24)

CVPR 2024: AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation

This repository contains MAPF-GPT, a deep learning-based model for solving MAPF problems. Trained with imitation learning on trajectories produced by LaCAM, it generates actions under partial observability without heuristics or agent communication. MAPF-GPT excels on unseen instances and outperforms state-of-the-art solvers.

Corresponding code of 'Quiros A.C.+, Coudray N.+, Yeaton A., Yang X., Chiriboga L., Karimkhan A., Narula N., Pass H., Moreira A.L., Le Quesne J.*, Tsirigos A.*, and Yuan K.* Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unlabeled, unannotated pathology slides. 2024'

An official code for paper: TFPred: Learning discriminative representations from unlabeled data for few-label rotating machinery fault diagnosis

LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding (AAAI-23)