[RA-L, 2024] The dataset for the paper: Learning self-supervised traversability with navigation experiences of mobile robots: A risk-aware self-training approach
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Updated
May 18, 2024 - CMake
[RA-L, 2024] The dataset for the paper: Learning self-supervised traversability with navigation experiences of mobile robots: A risk-aware self-training approach
[IJCAI 2023] Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
Pseudo-labeling for tabular data
This repository contains the implementation of the Self Meta Pseudo Labels (SMPL) method for semi-supervised learning
Code for my paper "Semi-Supervised Unconstrained Head Pose Estimation in the Wild"
A full pipeline AutoML tool for tabular data
code released for our ICML 2020 paper "Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation"
This repo contains implementation of uncertainty estimation, rectification, and minimization for guiding the pseudo-label learning in semi-supervised defect segmentation setting.
[IJCAI 2022] Official Pytorch code for paper “S2 Transformer for Image Captioning”
auto_labeler - An all-in-one library to automatically label vision data
[NeurIPS 2022] Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering
This repo contains implementation of semi-supervised defect segmentation based on pairwise similarity map consistency and ensemble-based cross pseudo labels
[IJCAI 2023] Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
The main objective of this repository is to become familiar with the task of Domain Adaptation applied to the Real-time Semantic Segmentation networks.
"In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning" by Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, Mubarak Shah (ICLR 2021)
Federated Semantic Segmentation with Fourier Domain Adaptation and Pseudo-labelling
"OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning" by Mamshad Nayeem Rizve, Navid Kardan, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah (ECCV 2022)
"Towards Realistic Semi-Supervised Learning" by Mamshad Nayeem Rizve, Navid Kardan, Mubarak Shah (ECCV 2022)
Unofficial Pytorch implementation of MaskCLIP
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