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 3926–3950 of 10307 papers

TitleStatusHype
Feature Based Methods in Domain Adaptation for Object Detection: A Review Paper—0
A Multi-media Approach to Cross-lingual Entity Knowledge Transfer—0
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection—0
Feature Corrective Transfer Learning: End-to-End Solutions to Object Detection in Non-Ideal Visual Conditions—0
Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning—0
Feature Denoising Diffusion Model for Blind Image Quality Assessment—0
Feature discriminativity estimation in CNNs for transfer learning—0
Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation—0
Decomposable Probability-of-Success Metrics in Algorithmic Search—0
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks—0
Adaptive Transfer Learning for Plant Phenotyping—0
Feature Interaction Fusion Self-Distillation Network For CTR Prediction—0
Fractals as Pre-training Datasets for Anomaly Detection and Localization—0
Feature matching as improved transfer learning technique for wearable EEG—0
A Segmentation Foundation Model for Diverse-type Tumors—0
Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-—0
Features are fate: a theory of transfer learning in high-dimensional regression—0
Feature Space Transfer for Data Augmentation—0
Feature-Supervised Action Modality Transfer—0
Feature Transfer Learning for Deep Face Recognition with Under-Represented Data—0
Feature Transfer Learning for Face Recognition With Under-Represented Data—0
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification—0
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning—0
From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks—0
From Macro to Micro: Boosting micro-expression recognition via pre-training on macro-expression videos—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