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 951–975 of 10307 papers

TitleStatusHype
Federated Domain Generalization via Prompt Learning and AggregationCode0
Domain Adaptation-based Edge Computing for Cross-Conditions Fault Diagnosis—0
Causal Time-Series Synchronization for Multi-Dimensional Forecasting—0
A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning—0
A Practical Guide to Fine-tuning Language Models with Limited Data—0
P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs—0
Edge Caching Optimization with PPO and Transfer Learning for Dynamic Environments—0
Mono2Stereo: Monocular Knowledge Transfer for Enhanced Stereo Matching—0
Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publicationsCode0
Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing ImageryCode0
Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images—0
A Composite Fault Diagnosis Model for NPPs Based on Bayesian-EfficientNet Module—0
Transfer Learning Guided Noise Reduction for Automatic Modulation Classification—0
MVKTrans: Multi-View Knowledge Transfer for Robust Multiomics ClassificationCode0
DEEGITS: Deep Learning based Framework for Measuring Heterogenous Traffic State in Challenging Traffic Scenarios—0
Federated Graph Learning with Graphless Clients—0
Zero-shot Cross-lingual Transfer Learning with Multiple Source and Target Languages for Information Extraction: Language Selection and Adversarial Training—0
Comprehensive and Comparative Analysis between Transfer Learning and Custom Built VGG and CNN-SVM Models for Wildfire Detection—0
Feature Interaction Fusion Self-Distillation Network For CTR Prediction—0
Quantifying Knowledge Distillation Using Partial Information Decomposition—0
LLM-Neo: Parameter Efficient Knowledge Distillation for Large Language ModelsCode1
DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning—0
MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning—0
CULL-MT: Compression Using Language and Layer pruning for Machine Translation—0
Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior PredictionCode0
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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