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 7151–7175 of 10307 papers

TitleStatusHype
Transfer Learning and Augmentation for Word Sense Disambiguation—0
Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset—0
Applying Transfer Learning for Improving Domain-Specific Search Experience Using Query to Question Similarity—0
Learn Dynamic-Aware State Embedding for Transfer Learning—0
Phase Transitions in Transfer Learning for High-Dimensional Perceptrons—0
A Robust Illumination-Invariant Camera System for Agricultural Applications—0
End-to-End Video Question-Answer Generation with Generator-Pretester NetworkCode0
An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic—0
COVID-19: Comparative Analysis of Methods for Identifying Articles Related to Therapeutics and Vaccines without Using Labeled Data—0
A Framework for Fast Scalable BNN Inference using Googlenet and Transfer Learning—0
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection—0
Coreference Resolution in Research Papers from Multiple DomainsCode0
Towards Network Traffic Monitoring Using Deep Transfer Learning—0
Exploring Transfer Learning on Face Recognition of Dark Skinned, Low Quality and Low Resource Face Data—0
Comparative study on different Deep Learning models for Skin Lesion Classification using transfer learning approachCode0
AT-GAN: An Adversarial Generative Model for Non-constrained Adversarial Examples—0
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning—0
Adaptive Adversarial Network for Source-Free Domain Adaptation—0
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit—0
An Euler-based GAN for time series—0
Explicit Connection Distillation—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
Continuous Transfer Learning—0
P-Swish: Activation Function with Learnable Parameters Based on Swish Activation Function in Deep Learning—0
Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face 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