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 1726–1750 of 10307 papers

TitleStatusHype
Transfer Learning Approach for Railway Technical Map (RTM) Component Identification—0
Prompt-Based Spatio-Temporal Graph Transfer LearningCode0
Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGANCode0
Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction—0
Modeling citation worthiness by using attention-based bidirectional long short-term memory networks and interpretable modelsCode0
Towards Graph Contrastive Learning: A Survey and Beyond—0
Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed AdaptationCode1
Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning—0
Learning More Generalized Experts by Merging Experts in Mixture-of-Experts—0
Computer Vision in the Food Industry: Accurate, Real-time, and Automatic Food Recognition with Pretrained MobileNetV2—0
Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic AnchorsCode1
Exploring speech style spaces with language models: Emotional TTS without emotion labels—0
Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations—0
Subject-Adaptive Transfer Learning Using Resting State EEG Signals for Cross-Subject EEG Motor Imagery ClassificationCode1
DeepPavlov at SemEval-2024 Task 8: Leveraging Transfer Learning for Detecting Boundaries of Machine-Generated TextsCode0
Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases AutosegmentationCode0
Dynamic data sampler for cross-language transfer learning in large language modelsCode7
Probabilistic transfer learning methodology to expedite high fidelity simulation of reactive flows—0
Confidence Estimation in Unsupervised Deep Change Vector Analysis—0
Monaural speech enhancement on drone via Adapter based transfer learning—0
Biasing & Debiasing based Approach Towards Fair Knowledge Transfer for Equitable Skin Analysis—0
Continuous Transfer Learning for UAV Communication-aware Trajectory Design—0
A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments—0
Fully Distributed Fog Load Balancing with Multi-Agent Reinforcement Learning—0
Deep Learning in Earthquake Engineering: A Comprehensive Review—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