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 3201–3225 of 10307 papers

TitleStatusHype
DoQA -- Accessing Domain-Specific FAQs via Conversational QA—0
DoQA - Accessing Domain-Specific FAQs via Conversational QA—0
Dose Prediction with Deep Learning for Prostate Cancer Radiation Therapy: Model Adaptation to Different Treatment Planning Practices—0
Do sound event representations generalize to other audio tasks? A case study in audio transfer learning—0
Do the Frankenstein, or how to achieve better out-of-distribution performance with manifold mixing model soup—0
Double-Dip: Thwarting Label-Only Membership Inference Attacks with Transfer Learning and Randomization—0
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks—0
DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and Rendering—0
DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain—0
Double Transfer Learning for Breast Cancer Histopathologic Image Classification—0
Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals—0
A Comparison of Methods for Neural Network Aggregation—0
Do We Really Need a Large Number of Visual Prompts?—0
DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASR—0
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency—0
Automatic segmentation of texts into units of meaning for reading assistance—0
Meta-models for transfer learning in source localisation—0
DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM—0
DreamBeast: Distilling 3D Fantastical Animals with Part-Aware Knowledge Transfer—0
Novel coronavirus pneumonia lesion segmentation in CT images—0
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments—0
DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading—0
Analysis and Prediction of NLP models via Task Embeddings—0
Encoding Explanatory Knowledge for Zero-shot Science Question Answering—0
Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors—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