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 5251–5275 of 10307 papers

TitleStatusHype
Generalization in Neural Networks: A Broad Survey—0
Class-Specific Channel Attention for Few-Shot Learning—0
Classification of Breast Tumours Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting MethodsCode0
Gender bias Evaluation in Luganda-English Machine Translation—0
Public Parking Spot Detection And Geo-localization Using Transfer Learning—0
A Transferable Multi-stage Model with Cycling Discrepancy Learning for Lithium-ion Battery State of Health EstimationCode0
Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking—0
Razmecheno: Named Entity Recognition from Digital Archive of Diaries “Prozhito”—0
Low Resource Chat Translation: A Benchmark for Hindi–English Language PairCode0
Few-shot Adaptive Object Detection with Cross-Domain CutMix—0
Classical-to-quantum convolutional neural network transfer learning—0
DLDNN: Deterministic Lateral Displacement Design Automation by Neural NetworksCode0
Real-Time Mask Detection Based on SSD-MobileNetV2—0
Exploring Semantic Attributes from A Foundation Model for Federated Learning of Disjoint Label Spaces—0
Detection and Classification of Brain tumors Using Deep Convolutional Neural Networks—0
Removing Rain Streaks via Task Transfer Learning—0
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck—0
Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning—0
Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning—0
Cross-lingual Transfer Learning for Fake News Detector in a Low-Resource LanguageCode0
MuLan: A Joint Embedding of Music Audio and Natural LanguageCode0
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages—0
Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression—0
Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge TransferCode0
Image augmentation improves few-shot classification performance in plant disease recognition—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