transfer-learning

Flood detection from images using deep learning. Deep learning library KERAS was employed and MobileNet architecture was fine-tuned for image classification task.

This source code implements our ECCV paper "task-conditioned domain adaptation for pedestrian detection in thermal imagery".

This repository uses pretrain BERT embeddings for transfer learning in QA domain

PLMFit platform for TL on PLMs

ISIC 2019 - Skin Lesion Analysis Towards Melanoma Detection

The traditional machine learning models give a lot of pain when we do not have sufficient labeled data for the specific task or domain we care about to train a reliable model. Transfer learning allows us to deal with these scenarios by leveraging the already existing labeled data of some related task or domain. We try to store this knowledge gained in solving the source task in the source domain and apply it to our problem of interest. In this work, I have utilized Transfer Learning utilizing BertForSequenceClassification model. Also tried RobertaForSequenceClassification and XLNetForSequenceClassification models for Fine-Tuning the Model.

Official Implementation of "Fine-Tuning is Fine, if Calibrated.", NeurIPS 2024

Code for reproducing the experiments on large-scale pre-training and transfer learning for the paper "Effect of large-scale pre-training on full and few-shot transfer learning for natural and medical images" (https://arxiv.org/abs/2106.00116)

Implementation of TSDS: Data Selection for Task-Specific Model Finetuning. An optimal-transport framework for selecting domain-specific and task-specific training data to improve LLM finetuning and instruction tuning.

A curated list of papers on graph transfer learning (GTL).

Code and supplementary materials for the research paper titled 'Advancing Geological Image Segmentation: Deep Learning Approaches for Rock Type Identification and Classification', published in Applied Computing and Geosciences (Elsevier).

A step-by-step PyTorch guide for improved vehicle type 2D object detection based on Faster-RCNN using transfer learning and data augmentation.

Forest Fire Detection By Convolutional Neural Network

Breast cancer classification from Mammogram images using Deep CNN with Keras and TensorFlow.

Deep Learning and Computer Vision application for chessboard detection and chess pieces classification.

An application that summarizes and classifies long text using zero-shot transfer-learning hosted on Huggingface spaces.

This project aims to develop an innovative anomaly detection system using advanced data mining and deep learning techniques to accurately identify and localize defects in manufacturing components, thereby enhancing quality control processes and reducing production losses.

A repo for NLP model Bert.

Wrapper for stable version of RoBERTa language models

Fine-tuning Mask2Former on the FoodSeg103 dataset for food segmentation, with deployment and interactive inference via Gradio.