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 1176–1200 of 10307 papers

TitleStatusHype
Transfer Learning for E-commerce Query Product Type Prediction—0
Transfer Learning and Double U-Net Empowered Wave Propagation Model in Complex Indoor Environment—0
MaPPER: Multimodal Prior-guided Parameter Efficient Tuning for Referring Expression ComprehensionCode1
Cross-Domain Knowledge Transfer for Underwater Acoustic Classification Using Pre-trained ModelsCode1
Transfer Learning with Clinical Concept Embeddings from Large Language Models—0
E-Sort: Empowering End-to-end Neural Network for Multi-channel Spike Sorting with Transfer Learning and Fast Post-processingCode0
Rapid aerodynamic prediction of swept wings via physics-embedded transfer learning—0
Exploring bat song syllable representations in self-supervised audio encoders—0
Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability—0
Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data—0
Recognition of Harmful Phytoplankton from Microscopic Images using Deep Learning—0
Using Large Language Models to Generate Clinical Trial Tables and Figures—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
Bridging Domain Gap for Flight-Ready Spaceborne Vision—0
All-in-one foundational models learning across quantum chemical levelsCode2
Development and bilingual evaluation of Japanese medical large language model within reasonably low computational resourcesCode1
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
OmniGen: Unified Image GenerationCode7
Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation—0
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
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
On the Generalizability of Foundation Models for Crop Type MappingCode0
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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