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 2776–2800 of 10307 papers

TitleStatusHype
Sharpness-Aware Cross-Domain Recommendation to Cold-Start Users—0
Using LLMs to Establish Implicit User Sentiment of Software Desirability—0
Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic FeaturesCode0
IAI Group at CheckThat! 2024: Transformer Models and Data Augmentation for Checkworthy Claim DetectionCode0
Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual LearningCode0
UNER: A Unified Prediction Head for Named Entity Recognition in Visually-rich Documents—0
Cross-domain Named Entity Recognition via Graph Matching—0
Distance-Preserving Spatial Representations in Genomic Data—0
A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life—0
Efficient Patient Fine-Tuned Seizure Detection with a Tensor Kernel Machine—0
An Explainable Vision Transformer with Transfer Learning Combined with Support Vector Machine Based Efficient Drought Stress Identification—0
EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching—0
Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population—0
Image-based Detection of Segment Misalignment in Multi-mirror Satellites using Transfer Learning—0
Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning—0
DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis—0
ActivityCLIP: Enhancing Group Activity Recognition by Mining Complementary Information from Text to Supplement Image Modality—0
Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework—0
Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning—0
ProRuka: A highly efficient HMI algorithm for controlling a novel prosthetic hand with 6-DOF using sonomyography—0
Unmasking unlearnable models: a classification challenge for biomedical images without visible cues—0
EXIT: An EXplicit Interest Transfer Framework for Cross-Domain Recommendation—0
Detached and Interactive Multimodal LearningCode0
Can Modifying Data Address Graph Domain Adaptation?Code0
Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual PersistenceCode0
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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