Semi-Supervised Learning with Pseudo-Labeling

Semi-supervised learning techniques (pseudo-label, mixmatch, and co-training) for pre-trained BERT language model amidst low-data regime based on molecular SMILES from the Molecule Net benchmark.

Multiple Generation Based Knowledge Distillation: A Roadmap

[IEEE TII] On-Device Saliency Prediction Based on Pseudoknowledge Distillation

The main objective of this repository is to become familiar with the task of Domain Adaptation applied to the Real-time Semantic Segmentation networks.

Probabilistic Domain Adaptation for Biomedical Image Segmentation

Pseudo Labelling on MNIST dataset in Tensorflow 2.x

Accompanying notebook and sources to "A Guide to Pseudolabelling: How to get a Kaggle medal with only one model" (Dec. 2020 PyData Boston-Cambridge Keynote)

This repository contains code for the paper "Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation", published at IEEE JBHI 2022

[IEEE TETCI] "ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training"

Pseudo-Label: Semi-Supervised Learning on CIFAR-10 in Keras

[TPAMI 2023] Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)

PseudoLabel 2013, VAT, PI model, Tempens, MeanTeacher, ICT, MixMatch, FixMatch