SOTAVerified

Transfer Learning

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 )

Papers

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Showing 7776–7800 of 10307 papers

TitleStatusHype
Improved Reinforcement Learning in Cooperative Multi-agent Environments Using Knowledge Transfer—0
Multi-agent Reinforcement Learning with Sparse Interactions by Negotiation and Knowledge Transfer—0
Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems—0
Multi-Agent Transfer Learning via Temporal Contrastive Learning—0
A Mamba Foundation Model for Time Series Forecasting—0
MultiBalance: Multi-Objective Gradient Balancing in Industrial-Scale Multi-Task Recommendation System—0
The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks—0
Multi-batch Reinforcement Learning via Sample Transfer and Imitation Learning—0
Multi-Cell Compositional LSTM for NER Domain Adaptation—0
Multicenter automatic detection of invasive carcinoma on breast whole slide images—0
Domain generalization in deep learning for contrast-enhanced imaging—0
Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions—0
Multichannel Attention Networks with Ensembled Transfer Learning to Recognize Bangla Handwritten Charecter—0
Multi-Channel CNN-based Object Detection for Enhanced Situation Awareness—0
Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19—0
A Macro- and Micro-Hierarchical Transfer Learning Framework for Cross-Domain Fake News Detection—0
Multi-Classification of Brain Tumor Images Using Transfer Learning Based Deep Neural Network—0
Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks—0
Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network—0
Multi-Class Zero-Shot Learning for Artistic Material Recognition—0
MultiConfederated Learning: Inclusive Non-IID Data handling with Decentralized Federated Learning—0
ACES -- Automatic Configuration of Energy Harvesting Sensors with Reinforcement Learning—0
Multi-Dataset Multi-Task Learning for COVID-19 Prognosis—0
Multi-dataset synergistic in supervised learning to pre-label structural components in point clouds from shell construction scenes—0
A machine learning approach to using Quality-of-Life patient scores in guiding prostate radiation therapy dosing—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified