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

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Showing 1376–1400 of 10307 papers

TitleStatusHype
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
UNER: A Unified Prediction Head for Named Entity Recognition in Visually-rich Documents—0
Cross-domain Named Entity Recognition via Graph Matching—0
Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual LearningCode0
Distance-Preserving Spatial Representations in Genomic Data—0
Scaling Backwards: Minimal Synthetic Pre-training?Code1
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
EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching—0
An Explainable Vision Transformer with Transfer Learning Combined with Support Vector Machine Based Efficient Drought Stress Identification—0
Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population—0
DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis—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
ProRuka: A highly efficient HMI algorithm for controlling a novel prosthetic hand with 6-DOF using sonomyography—0
Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning—0
Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework—0
ActivityCLIP: Enhancing Group Activity Recognition by Mining Complementary Information from Text to Supplement Image Modality—0
EXIT: An EXplicit Interest Transfer Framework for Cross-Domain Recommendation—0
Unmasking unlearnable models: a classification challenge for biomedical images without visible cues—0
Detached and Interactive Multimodal LearningCode0
Can Modifying Data Address Graph Domain Adaptation?Code0
PP-TIL: Personalized Planning for Autonomous Driving with Instance-based Transfer Imitation LearningCode1
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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