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 5601–5625 of 10307 papers

TitleStatusHype
Quantum Transfer Learning for Wi-Fi Sensing—0
HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer—0
When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer LearningCode0
Manifold Characteristics That Predict Downstream Task PerformanceCode0
Fused Deep Neural Network based Transfer Learning in Occluded Face Classification and Person re-Identification—0
Revisiting Facial Key Point Detection: An Efficient Approach Using Deep Neural Networks—0
Classification of Astronomical Bodies by Efficient Layer Fine-Tuning of Deep Neural NetworksCode0
Efficient Deep Learning Methods for Identification of Defective Casting ProductsCode0
Improving Neural Machine Translation of Indigenous Languages with Multilingual Transfer Learning—0
Exploring the structure-property relations of thin-walled, 2D extruded lattices using neural networks—0
Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation—0
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities—0
How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time AugmentationCode0
Toward a Geometrical Understanding of Self-supervised Contrastive Learning—0
D3T-GAN: Data-Dependent Domain Transfer GANs for Few-shot Image Generation—0
Target Aware Network Architecture Search and Compression for Efficient Knowledge TransferCode0
DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
SimCPSR: Simple Contrastive Learning for Paper Submission Recommendation SystemCode0
Automatic Tuberculosis and COVID-19 cough classification using deep learning—0
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning—0
CoDo: Contrastive Learning with Downstream Background Invariance for Detection—0
Object Detection in Indian Food Platters using Transfer Learning with YOLOv4—0
An Effective Scheme for Maize Disease Recognition based on Deep Networks—0
Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulenceCode0
Sub-Word Alignment Is Still Useful: A Vest-Pocket Method for Enhancing Low-Resource Machine TranslationCode0
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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