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 2201–2225 of 10307 papers

TitleStatusHype
Precise Knowledge Transfer via Flow Matching—0
Transfer Learning in ECG Diagnosis: Is It Effective?Code0
Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A SurveyCode4
Online Transfer Learning for RSV Case Detection—0
Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene ClassificationCode1
Analyzing the Evaluation of Cross-Lingual Knowledge Transfer in Multilingual Language Models—0
Exploring the Robustness of Task-oriented Dialogue Systems for Colloquial German VarietiesCode0
InceptionCapsule: Inception-Resnet and CapsuleNet with self-attention for medical image Classification—0
Cooperative Knowledge Distillation: A Learner Agnostic ApproachCode0
Speech foundation models in healthcare: Effect of layer selection on pathological speech feature predictionCode0
cmaes : A Simple yet Practical Python Library for CMA-ESCode3
Double-Dip: Thwarting Label-Only Membership Inference Attacks with Transfer Learning and Randomization—0
Disentangling the Roles of Target-Side Transfer and Regularization in Multilingual Machine Translation—0
Graph Domain Adaptation: Challenges, Progress and ProspectsCode2
Domain-Independent Deception: A New Taxonomy and Linguistic Analysis—0
Efficient Fine-tuning of Audio Spectrogram Transformers via Soft Mixture of AdaptersCode1
Control-Theoretic Techniques for Online Adaptation of Deep Neural Networks in Dynamical Systems—0
MelNet: A Real-Time Deep Learning Algorithm for Object Detection—0
Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks—0
Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training—0
Finetuning Large Language Models for Vulnerability DetectionCode2
Transfer Learning for Text Diffusion Models—0
Multiple Yield Curve Modeling and Forecasting using Deep Learning—0
Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending—0
MV2MAE: Multi-View Video Masked Autoencoders—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