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 5976–6000 of 10307 papers

TitleStatusHype
Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer—0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?—0
One Step Is Enough for Few-Shot Cross-Lingual Transfer: Co-Training with Gradient Optimization—0
Pruning Adatperfusion with Lottery Ticket Hypothesis—0
Addressing the Challenges of Cross-Lingual Hate Speech Detection—0
Semantic decoupled representation learning for remote sensing image change detection—0
Deep Optimal Transport for Domain Adaptation on SPD ManifoldsCode0
Transferability in Deep Learning: A Survey—0
Towards Zero-shot Sign Language Recognition—0
Adaptive Transfer Learning for Plant Phenotyping—0
CLUE: Contextualised Unified Explainable Learning of User Engagement in Video Lectures—0
Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP—0
Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and ClusteringCode0
Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision—0
Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents—0
Intra-domain and cross-domain transfer learning for time series data -- How transferable are the features?—0
Early Diagnosis of Parkinsons Disease by Analyzing Magnetic Resonance Imaging Brain Scans and Patient Characteristics—0
The Recurrent Reinforcement Learning Crypto Agent—0
A Feature Extraction based Model for Hate Speech Identification—0
Classification of Beer Bottles using Object Detection and Transfer Learning—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy—0
Transfer Learning for Scene Text Recognition in Indian Languages—0
BERT for Sentiment Analysis: Pre-trained and Fine-Tuned AlternativesCode0
Data-Efficient Information Extraction from Form-Like Documents—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