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 6551–6575 of 10307 papers

TitleStatusHype
Indoor Localization Under Limited Measurements: A Cross-Environment Joint Semi-Supervised and Transfer Learning Approach—0
From augmented microscopy to the topological transformer: a new approach in cell image analysis for Alzheimer's research—0
Cross-Modal Analysis of Human Detection for Robotics: An Industrial Case Study—0
Semi-Supervising Learning, Transfer Learning, and Knowledge Distillation with SimCLR—0
Robustness of convolutional neural networks to physiological ECG noise—0
Multilevel Knowledge Transfer for Cross-Domain Object Detection—0
Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learningCode0
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack—0
Cross-Modal Knowledge Transfer via Inter-Modal Translation and Alignment for Affect Recognition—0
A Hinge-Loss based Codebook Transfer for Cross-Domain Recommendation with Nonoverlapping Data—0
Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store ReviewsCode0
UoB\_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.—0
Zero-shot Event Extraction via Transfer Learning: Challenges and Insights—0
Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach—0
The UCF Systems for the LoResMT 2021 Machine Translation Shared Task—0
Edinburgh’s End-to-End Multilingual Speech Translation System for IWSLT 2021—0
Script Parsing with Hierarchical Sequence Modelling—0
Love Thy Neighbor: Combining Two Neighboring Low-Resource Languages for Translation—0
Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-TuningCode0
Multi-View Cross-Lingual Structured Prediction with Minimum Supervision—0
Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection—0
Multilingual Dependency Parsing for Low-Resource African Languages: Case Studies on Bambara, Wolof, and Yoruba—0
MinD at SemEval-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal Fusion—0
IMS’ Systems for the IWSLT 2021 Low-Resource Speech Translation Task—0
Improved pronunciation prediction accuracy using morphology—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