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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5751–5775 of 10307 papers

TitleStatusHype
Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation—0
Domain Adaptation for Sparse-Data Settings: What Do We Gain by Not Using Bert?—0
CycDA: Unsupervised Cycle Domain Adaptation from Image to VideoCode0
Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks—0
Enabling hand gesture customization on wrist-worn devices—0
Transfer Learning Framework for Low-Resource Text-to-Speech using a Large-Scale Unlabeled Speech Corpus—0
Using Domain Knowledge for Low Resource Named Entity Recognition—0
Towards Transferable Speech Emotion Representation: On loss functions for cross-lingual latent representations—0
A Framework of Meta Functional Learning for Regularising Knowledge Transfer—0
Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison—0
Benchmarking Algorithms for Automatic License Plate Recognition—0
Medicinal Boxes Recognition on a Deep Transfer Learning Augmented Reality Mobile Application—0
Transfer of codebook latent factors for cross-domain recommendation with non-overlapping data—0
Interpretation of Chest x-rays affected by bullets using deep transfer learning—0
Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy imagesCode0
Digital Fingerprinting of Microstructures—0
Learning Losses for Strategic Classification—0
Facial Action Unit Recognition Based on Transfer Learning—0
A Comparative Evaluation Of Transformer Models For De-Identification Of Clinical Text Data—0
Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining?Code0
A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study—0
Visuo-Haptic Object Perception for Robots: An Overview—0
A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework—0
Feature Distribution Matching for Federated Domain GeneralizationCode0
A Locally Adaptive Algorithm for Multiple Testing with Network Structure—0
Show:102550
← PrevPage 231 of 413Next →
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