[CVPR'22 & IJCV'24] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels & Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation
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Updated
Aug 20, 2024 - Python
[CVPR'22 & IJCV'24] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels & Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation
[PVLDB 2024 Best Paper Nomination] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
Python code for abnormal detection using Support Vector Data Description (SVDD)
The python verification code processing library pycapt supports extremely convenient verification code preprocessing and generation, and assists machine learning in automatically generating training sets.
(new released v2.5 LTS.2022-06-25) It has focused on developing an All in One operating system for programming, designing and data science.Emperor-OS has over 500 apps and important tools
推箱子解决方案 sokoban AI solver
Explore the latest AI Agent Framework!
[CVPR 2024] Customize your NeRF: Adaptive Source Driven 3D Scene Editing via Local-Global Iterative Training
SouPyX: An Audio Exploration Space.🪐
Upload & Merge CSV or JSON Data with Images to Notion Database
🧬 bp-ga algorithm implemented by pytorch
SPAM-Detection-Model Is A NLP Model To Detect SPAM Messages...
Data sets on prognosis and health management(PHM相关数据集)
机器学习笔记本 Mechine Learning notebook
This repository is the 4th solution for competition Herbarium 2020 - FGVC7 https://www.kaggle.com/c/herbarium-2020-fgvc7/overview
Official PyTorch implementation for "Classifying Nodes in Graphs without GNNs"
Anggota Kelompok Artificial Intelligence (AI): Ekalma Toto Alloy Sembiring (211401084), Virgie Beatrice (221401034), Mutiara Aprilia (221401115), Nirmala Aizya Agatha Silalahi (221401118)
AI-driven credit card scanning for cross-platform apps built with Capacitor.
The project focuses on utilizing federated machine learning to enhance the detection of malware in Internet of Things (IoT) devices. The code includes experiments simulating various configurations where clients collaboratively train deep learning models for malware detection without sharing raw data.
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