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 6651–6675 of 10307 papers

TitleStatusHype
A Deep Transfer Learning Approach on Identifying Glitch Wave-form in Gravitational Wave Data—0
Contradiction Detection in Persian Text—0
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning—0
Dealing with Adversarial Player Strategies in the Neural Network Game iNNk through Ensemble Learning—0
Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning—0
Isotonic Data Augmentation for Knowledge Distillation—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
Learning from scarce information: using synthetic data to classify Roman fine ware pottery—0
Parasitic Egg Detection and Classification in Low-cost Microscopic Images using Transfer Learning—0
Empirically Measuring Transfer Distance for System Design and Operation—0
Disentangling Transfer and Interference in Multi-Domain Learning—0
A Systems Theory of Transfer Learning—0
Language Identification of Hindi-English tweets using code-mixed BERT—0
Toward Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learning—0
Learning to See before Learning to Act: Visual Pre-training for Manipulation—0
Recent Neural Methods on Dialogue State Tracking for Task-Oriented Dialogue Systems: A Survey—0
Cross-Lingual Transfer Learning for Statistical Type Inference—0
A Primer on Pretrained Multilingual Language Models—0
A Comparison between Named Entity Recognition Models in the Biomedical DomainCode0
CityNet: A Comprehensive Multi-Modal Urban Dataset for Advanced Research in Urban ComputingCode0
IMS' Systems for the IWSLT 2021 Low-Resource Speech Translation Task—0
Extraction of Key-frames of Endoscopic Videos by using Depth Information—0
Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning—0
When Video Classification Meets Incremental Classes—0
Zoo-Tuning: Adaptive Transfer from a Zoo of Models—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