Domain Adaptation / Transfer Learning in popular datasets (MNIST, SVHN, USPS)
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Updated
Jun 29, 2024 - Jupyter Notebook
Domain Adaptation / Transfer Learning in popular datasets (MNIST, SVHN, USPS)
📃🎉 Additional datasets for tensorflow.keras
IJCAI 2024, InfoMatch: Entropy neural estimation for semi-supervised image classification
Domain Adaptation With Domain-Adversarial Training of Neural Networks
Connection Reduction of DenseNet for Image Recognition
GUI for training an ML-algorithm (TensorFlow) with the SVHN-dataset.
This repository contains code for the PhD thesis: "A Study of Self-training Variants for Semi-supervised Image Classification" and publications.
Python experiments for https://arxiv.org/abs/1904.12286.
Implementation of semi and self supervised learning on Imbalanced Dataset
Four digit SVHN (Street View House Number) sequence prediction with CNN using Keras with TensorFlow backend
Training using an alternative approach: forward-thinking
Deep-digit-detector (and recognizer) in natural scene. A digit detection framework was implemented using keras with tensorflow backend.
Associative Domain Adaptation
PyTorch Implementation of InfoGAN
'Deploying machine learning models with a Flask API' tutorial, written for HyperionDev
A TensorFlow implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks (http://arxiv.org/pdf/1312.6082.pdf)
Convolutional Neural Networks for classification with the SVHN dataset.
Example of how to get data from SVHN dataset and visualize it.
Add a description, image, and links to the svhn topic page so that developers can more easily learn about it.
To associate your repository with the svhn topic, visit your repo's landing page and select "manage topics."