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 3126–3150 of 10307 papers

TitleStatusHype
Knowledge Distillation via Token-level Relationship Graph—0
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—0
Inter-Cell Network Slicing With Transfer Learning Empowered Multi-Agent Deep Reinforcement Learning—0
Meta-Analysis of Transfer Learning for Segmentation of Brain Lesions—0
MuDPT: Multi-modal Deep-symphysis Prompt Tuning for Large Pre-trained Vision-Language ModelsCode0
Masking meets Supervision: A Strong Learning AllianceCode1
MSVD-Indonesian: A Benchmark for Multimodal Video-Text Tasks in IndonesianCode0
BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous DatasetsCode1
Knowledge Transfer for Dynamic Multi-objective Optimization with a Changing Number of Objectives—0
Knowledge Transfer-Driven Few-Shot Class-Incremental LearningCode0
Transformer Training Strategies for Forecasting Multiple Load Time SeriesCode0
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotationsCode1
Learning-based sound speed estimation and aberration correction in linear-array photoacoustic imagingCode0
Persian Semantic Role Labeling Using Transfer Learning and BERT-Based Models—0
Text-Driven Foley Sound Generation With Latent Diffusion ModelCode0
Neural Priming for Sample-Efficient AdaptationCode1
Parameter-efficient is not sufficient: Exploring Parameter, Memory, and Time Efficient Adapter Tuning for Dense Predictions—0
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient LearningCode1
Cross-corpus Readability Compatibility Assessment for English TextsCode0
Segment Any Point Cloud Sequences by Distilling Vision Foundation ModelsCode2
Modeling T1 Resting-State MRI Variants Using Convolutional Neural Networks in Diagnosis of OCDCode0
Understanding and Mitigating Extrapolation Failures in Physics-Informed Neural Networks—0
DocumentNet: Bridging the Data Gap in Document Pre-Training—0
A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images—0
Iterative self-transfer learning: A general methodology for response time-history prediction based on small dataset—0
Show:102550
← PrevPage 126 of 413Next →
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