Experimenting with CNN architectures for image classification and methods to improve training with small datasets (semi-supervised learning).
-
Updated
Jul 27, 2018 - Python
Experimenting with CNN architectures for image classification and methods to improve training with small datasets (semi-supervised learning).
Auto Semi-supervised Outlier Detection for Malicious Authentication Events
Advanced Scheduling Algorithm for Managing Pseudo Labels in Semi-Supervised Learning
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification
This is one of my micro project that aims to prepare image dataset from a video input and annotates it automatically.
Exercises from IT3030 V20
Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).
Implementation of Co-training Regressors (COREG) semi-supervised regression algorithm from Zhou and Li, 2005.
Dissertação de Mestrado apresentada ao Programa de Pós-Graduação em Informática - PPGI da Universidade Federal do Espírito Santo - UFES
Codebase accompanying the paper "Efficient Co-Regularised Least Squares Regression".
Semisupervised classification methods (SSC) with Spark-ML, study and implementation
Code implementation of our paper "Exploring Domain-specific Contrastive Learning with Consistency Regularization for Semi-supervised Medical Image Segmentation "
PyTorch implementation of Bayesian Graph Convolutional Networks using Neighborhood Random Walk Sampling to supplement my Honors Thesis.
Face Recognition Algorithm using Unsupervised and Semi-supervised techniques
Inner product natural graph factorization machine used in 'GEMSEC: Graph Embedding with Self Clustering' .
Source codes for the paper "Local Additivity Based Data Augmentation for Semi-supervised NER"
Reference implementation of Diffusion2Vec (Complenet 2018) built on Gensim and NetworkX.
Add a description, image, and links to the semisupervised-learning topic page so that developers can more easily learn about it.
To associate your repository with the semisupervised-learning topic, visit your repo's landing page and select "manage topics."