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 5676–5700 of 10307 papers

TitleStatusHype
Resource-efficient domain adaptive pre-training for medical images—0
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning—0
A Comprehensive Understanding of Code-mixed Language Semantics using Hierarchical TransformerCode0
Meta-Learning Based Early Fault Detection for Rolling Bearings via Few-Shot Anomaly Detection—0
CATrans: Context and Affinity Transformer for Few-Shot Segmentation—0
Transfer Learning with Pre-trained Conditional Generative Models—0
ISTRBoost: Importance Sampling Transfer Regression using Boosting—0
Meta-free few-shot learning via representation learning with weight averaging—0
Parkinson's disease diagnostics using AI and natural language knowledge transfer—0
A Hybrid Defense Method against Adversarial Attacks on Traffic Sign Classifiers in Autonomous Vehicles—0
Multi-objective Pointer Network for Combinatorial OptimizationCode0
Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy—0
Meta Transfer Learning for Early Success Prediction in MOOCsCode0
Improving Self-Supervised Learning-based MOS Prediction NetworksCode0
Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models—0
Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pod—0
Detecting early signs of depression in the conversational domain: The role of transfer learning in low-resource scenariosCode0
Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation—0
You Are What You Write: Preserving Privacy in the Era of Large Language Models—0
Detecting Text Formality: A Study of Text Classification ApproachesCode0
On The Cross-Modal Transfer from Natural Language to Code through Adapter ModulesCode0
Enhancing Non-mass Breast Ultrasound Cancer Classification With Knowledge Transfer—0
Hierarchical Optimal Transport for Comparing Histopathology Datasets—0
Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology—0
kpfriends at SemEval-2022 Task 2: NEAMER -- Named Entity Augmented Multi-word Expression Recognizer—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