Source code and data for the journal ``Dual learning for semi-supervised natural language understanding" in TASLP 2020.
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Updated
Apr 1, 2021 - Python
Source code and data for the journal ``Dual learning for semi-supervised natural language understanding" in TASLP 2020.
Keras implementation of "Densely Connected Convolutional Networks" applied to the Galaxy Zoo Kaggle competition.
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Unpublished paper focusing on the effects of pseudo-labeling without specialized models and supporting algorithms.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.
An Uncertainty-Aware Pseudo-Label Selection Framework using Regularized Conformal Prediction
[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.
Research paper-Enhancing action recognition with precondition and effect
Code for my paper "Semi-Supervised Unconstrained Head Pose Estimation in the Wild"
[Unofficial] Implementation of Pseudo-Label Guided Unsupervised Domain Adaptation of Contextual Embeddings (ACL Workshop 2021)
The dataset for the paper 'Learning self-supervised traversability with navigation experiences of mobile robots: A risk-aware self-training approach'
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
You can’t handle the (dirty) truth: Data-centric insights improve pseudo-labeling
Unofficial Pytorch implementation of MaskCLIP
Tensorflow based training, inference and feature engineering pipelines used in OSIC Kaggle Competition
IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscle Segmentation and Propagation in Volumetric Ultrasound
Federated Semantic Segmentation with Fourier Domain Adaptation and Pseudo-labelling
This repo contains implementation of uncertainty estimation, rectification, and minimization for guiding the pseudo-label learning in semi-supervised defect segmentation setting.
[ECCV2022] 3D-PL: Domain Adaptive Depth Estimation with 3D-aware Pseudo-Labeling
"Advanced Machine Learning" project @ Politecnico di Torino, a.y. 2021/2022.
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