Transfer Learning
2819 papers with code • 7 benchmarks • 14 datasets
Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.
( Image credit: Subodh Malgonde )
Benchmarks
These leaderboards are used to track progress in Transfer Learning
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Libraries
Use these libraries to find Transfer Learning models and implementationsDatasets
KITTI
Office-Home
Sims4Action
MFRC
CARLANE Benchmark
BanglaLekha-Isolated
SpaRTUN
fluocells
Retinal Fundus MultiDisease Image Dataset (RFMiD)
Subtasks
Latest papers with no code
Metric Learning for 3D Point Clouds Using Optimal Transport
Learning embeddings of any data largely depends on the ability of the target space to capture semantic rela- tions.
sEMG-based Fine-grained Gesture Recognition via Improved LightGBM Model
Compared with the scheme directly trained on small sample data, the recognition rate of transfer learning was significantly improved from 60. 35% to 78. 54%, effectively solving the problem of insufficient data, and proving the applicability and advantages of transfer learning in fine gesture recognition tasks for disabled people.
Feature Corrective Transfer Learning: End-to-End Solutions to Object Detection in Non-Ideal Visual Conditions
Our study introduces "Feature Corrective Transfer Learning", a novel approach that leverages transfer learning and a bespoke loss function to facilitate the end-to-end detection of objects in these challenging scenarios without the need to convert non-ideal images into their RGB counterparts.
Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI
Lung diseases remain a critical global health concern, and it's crucial to have accurate and quick ways to diagnose them.
Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification
We present a novel approach, Supervised Contrastive Vision Transformer (SupCon-ViT), for improving the classification of invasive ductal carcinoma in terms of accuracy and generalization by leveraging the inherent strengths and advantages of both transfer learning, i. e., pre-trained vision transformer, and supervised contrastive learning.
GenFighter: A Generative and Evolutive Textual Attack Removal
Adversarial attacks pose significant challenges to deep neural networks (DNNs) such as Transformer models in natural language processing (NLP).
Control Theoretic Approach to Fine-Tuning and Transfer Learning
Given a training set in the form of a paired $(\mathcal{X},\mathcal{Y})$, we say that the control system $\dot{x} = f(x, u)$ has learned the paired set via the control $u^*$ if the system steers each point of $\mathcal{X}$ to its corresponding target in $\mathcal{Y}$.
Neuron Specialization: Leveraging intrinsic task modularity for multilingual machine translation
Training a unified multilingual model promotes knowledge transfer but inevitably introduces negative interference.
Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal Transport
Based on this novel algorithm, we propose two new strategies for MSDA: GMM-WBT and GMM-DaDiL.
Privacy-Preserving Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems
On-device intelligence (ODI) enables artificial intelligence (AI) applications to run on end devices, providing real-time and customized AI services without relying on remote servers.
