Study project with experiments in Text MultiClassification Task Field (:
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Updated
May 13, 2021 - Jupyter Notebook
Study project with experiments in Text MultiClassification Task Field (:
Unpublished paper focusing on the effects of pseudo-labeling without specialized models and supporting algorithms. Includes presentation slides, paper, experiments and task-specific preprocessing/pseudo-labeling library.
[IJCAI 2023] Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
Code and dataset for our paper "Anchored Model Transfer and Soft Instance Transfer for Cross-Task Cross-Domain Learning: A Study Through Aspect-Level Sentiment Classification", WWW2020
Federated Semantic Segmentation with Fourier Domain Adaptation and Pseudo-labelling
The main objective of this repository is to become familiar with the task of Domain Adaptation applied to the Real-time Semantic Segmentation networks.
This repository contains the implementation of the Self Meta Pseudo Labels (SMPL) method for semi-supervised learning
Tensorflow based training, inference and feature engineering pipelines used in OSIC Kaggle Competition
Keras implementation of "Densely Connected Convolutional Networks" applied to the Galaxy Zoo Kaggle competition.
[Unofficial] Implementation of Pseudo-Label Guided Unsupervised Domain Adaptation of Contextual Embeddings (ACL Workshop 2021)
Unofficial implementation of Adversarial Learning for Semi-Supervised Semantic Segmentation with tensorflow/Keras
Classify dashcam images to detect cases of distracted or safe driving.
Code for my paper "Semi-Supervised Unconstrained Head Pose Estimation in the Wild"
You can’t handle the (dirty) truth: Data-centric insights improve pseudo-labeling
Unofficial Pytorch implementation of MaskCLIP
Research paper-Enhancing action recognition with precondition and effect
auto_labeler - An all-in-one library to automatically label vision data
An Uncertainty-Aware Pseudo-Label Selection Framework using Regularized Conformal Prediction
"Advanced Machine Learning" project @ Politecnico di Torino, a.y. 2021/2022.
This repo contains implementation of semi-supervised defect segmentation based on pairwise similarity map consistency and ensemble-based cross pseudo labels
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