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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3076–3100 of 10307 papers

TitleStatusHype
Just rotate it! Uncertainty estimation in closed-source models via multiple queries—0
Dynamically enhanced static handwriting representation for Parkinson's disease detection—0
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory—0
Near-Field Spot Beamfocusing: A Correlation-Aware Transfer Learning Approach—0
Prompt-Based Spatio-Temporal Graph Transfer LearningCode0
Transfer Learning Approach for Railway Technical Map (RTM) Component Identification—0
Modeling citation worthiness by using attention-based bidirectional long short-term memory networks and interpretable modelsCode0
Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGANCode0
Towards Graph Contrastive Learning: A Survey and Beyond—0
Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction—0
Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning—0
Computer Vision in the Food Industry: Accurate, Real-time, and Automatic Food Recognition with Pretrained MobileNetV2—0
Learning More Generalized Experts by Merging Experts in Mixture-of-Experts—0
Exploring speech style spaces with language models: Emotional TTS without emotion labels—0
Probabilistic transfer learning methodology to expedite high fidelity simulation of reactive flows—0
DeepPavlov at SemEval-2024 Task 8: Leveraging Transfer Learning for Detecting Boundaries of Machine-Generated TextsCode0
Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations—0
Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases AutosegmentationCode0
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
A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments—0
Continuous Transfer Learning for UAV Communication-aware Trajectory Design—0
Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls—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