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 2601–2625 of 10307 papers

TitleStatusHype
SeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer LearningCode0
DSG-KD: Knowledge Distillation from Domain-Specific to General Language ModelsCode0
From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks—0
Generalization in birdsong classification: impact of transfer learning methods and dataset characteristics—0
Multiple-Exit Tuning: Towards Inference-Efficient Adaptation for Vision Transformer—0
Transfer Learning with Clinical Concept Embeddings from Large Language Models—0
Transfer Learning and Double U-Net Empowered Wave Propagation Model in Complex Indoor Environment—0
Transfer Learning for E-commerce Query Product Type Prediction—0
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Tensorflow Pretrained Models—0
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
E-Sort: Empowering End-to-end Neural Network for Multi-channel Spike Sorting with Transfer Learning and Fast Post-processingCode0
Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data—0
Rapid aerodynamic prediction of swept wings via physics-embedded transfer learning—0
Exploring bat song syllable representations in self-supervised audio encoders—0
Recognition of Harmful Phytoplankton from Microscopic Images using Deep Learning—0
Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability—0
Using Large Language Models to Generate Clinical Trial Tables and Figures—0
Bridging Domain Gap for Flight-Ready Spaceborne Vision—0
Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network—0
Efficient Low-Resolution Face Recognition via Bridge Distillation—0
Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation—0
Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers—0
Leveraging Distillation Techniques for Document Understanding: A Case Study with FLAN-T5—0
RF-GML: Reference-Free Generative Machine Listener—0
A Comparative Study of Open Source Computer Vision Models for Application on Small Data: The Case of CFRP Tape Laying—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