Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
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Updated
Jul 9, 2024 - Python
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Image classification using CNN and transfer learning from pre-trained models for better performance.
This repository is the official implementation Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients.
Deep Learning and Transfer Learning Architectures for English Premier League Player Performance Forecasting: CS229 Final Project
Skin cancer classification using Transfer Learning and explainable AI
A Multi-Class Brain Tumor Classifier using Convolutional Neural Network with 99% Accuracy achieved by applying the method of Transfer Learning using Python and Pytorch Deep Learning Framework
Guide for setting up a virtual environment and kernel for Python image processing. Practical exercises cover face recognition, shapes detection, vehicle identification, face detection, and vehicle number plate detection in a Jupiter Notebook. Purpose: Hands-on experience with essential computer vision techniques.
To Detect and Classify Brain Tumors using CNN and ANN as an asset of Deep Learning and to examine the position of the tumor.
Interpretation of RNAseq experiments through robust, efficient comparison to public databases
Fashion Recommender : Powered by Transfer Learning
Obtaining a Heat Map of the areas most influential in sorting chest X-ray images.
created using transfer learning
Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.
Final Report - Project of Machine Learning_M2QF_University of Paris Saclay [https://www.universite-paris-saclay.fr/formation/master/mathematiques-et-applications/m2-finance-quantitative]
Implementation of various basic layers forward and back propagation. CS 231n Stanford Spring 2018: Convolutional Neural Networks for Visual Recognition. Solutions to Assignments
Trained a CNN model to classify cactus species (columnar cactus) in a binary classification challenge, hosted on Kaggle competition. Refined the model with image augmentation, pre-trained VGG16 model parameters using transfer learning, and improved the identification metric (area under ROC score) from 0.9964 to 0.9971 on the test set.
Simple CNN is a library that can be used to train and infer CNN models by use of PyTorch and ONNX.
Apply deep learning to detect and classify cracks. Prior to tensorflow framework and developed a GUI for deployment purposes.
Implementation of a machine learning model to predict COVID-19 Informative Text.
A transfer learning model to identify the breed of a dog from an image.It uses the trained model mobilenet.The modules used are Numpy , PIL , Tensorflow and tensorflow_hub
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