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 4176–4200 of 10307 papers

TitleStatusHype
AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction—0
Data-driven tool wear prediction in milling, based on a process-integrated single-sensor approach—0
Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning—0
Amortized Network Intervention to Steer the Excitatory Point Processes—0
Adaptive Sparse Transformer for Multilingual Translation—0
Autoencoding Features for Aviation Machine Learning Problems—0
Data-Driven Outage Restoration Time Prediction via Transfer Learning with Cluster Ensembles—0
Adaptive ship-radiated noise recognition with learnable fine-grained wavelet transform—0
Data-Driven Knowledge Transfer in Batch Q^* Learning—0
Data-driven inventory management for new products: An adjusted Dyna-Q approach with transfer learning—0
Autoencoder Based Sample Selection for Self-Taught Learning—0
Google is all you need: Semi-Supervised Transfer Learning Strategy For Light Multimodal Multi-Task Classification Model—0
Data-driven geophysics: from dictionary learning to deep learning—0
Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI—0
Autocorrect for Estonian texts: final report from project EKTB25—0
Data-driven Approaches to Surrogate Machine Learning Model Development—0
Auto-Compressing Networks—0
Adaptive Sample Aggregation In Transfer Learning—0
A Comparative Study of Western and Chinese Classical Music based on Soundscape Models—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
GradMix: Multi-source Transfer across Domains and Tasks—0
Graph Enabled Cross-Domain Knowledge Transfer—0
Data-Centric AI in the Age of Large Language Models—0
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference—0
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
Show:102550
← PrevPage 168 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