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 6051–6075 of 10307 papers

TitleStatusHype
Heterogeneous Federated Learning System for Sparse Healthcare Time-Series Prediction—0
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism—0
Heterogeneous Federated Learning via Personalized Generative Networks—0
Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning—0
Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends—0
Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization—0
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems—0
Heterogeneous Multi-task Metric Learning across Multiple Domains—0
Heterogeneous Representation Learning: A Review—0
Heterogeneous domain adaptation: An unsupervised approach—0
Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approach—0
Heterogeneous transfer learning for high dimensional regression with feature mismatch—0
Heterogeneous Transfer Learning in Ensemble Clustering—0
Self-Supervised Intrinsic Image Decomposition—0
Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process—0
Template-based Approach to Zero-shot Intent Recognition—0
Hey AI Can You Grade My Essay?: Automatic Essay Grading—0
HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast—0
Artificial Intelligence for Dementia Research Methods Optimization—0
Hierarchical Continual Reinforcement Learning via Large Language Model—0
Hidden Layers in Perceptual Learning—0
Hidden Markov Models and their Application for Predicting Failure Events—0
Hidden Markov tree models for semantic class induction—0
FacLens: Transferable Probe for Foreseeing Non-Factuality in Large Language Models—0
Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning—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