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 4276–4300 of 10307 papers

TitleStatusHype
An information-Theoretic Approach to Semi-supervised Transfer Learning—0
VBSF-TLD: Validation-Based Approach for Soft Computing-Inspired Transfer Learning in Drone Detection—0
Enhancing Low Resource NER Using Assisting Language And Transfer Learning—0
Understanding the Benefits of Image Augmentations—0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—0
Emotion Detection from EEG using Transfer Learning—0
SARN: Structurally-Aware Recurrent Network for Spatio-Temporal DisaggregationCode0
Customizing General-Purpose Foundation Models for Medical Report Generation—0
T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text ClassificationCode0
Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes—0
Transfer Learning from Pre-trained Language Models Improves End-to-End Speech Summarization—0
Transfer Learning of Transformer-based Speech Recognition Models from Czech to Slovak—0
XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning RepresentationsCode0
AutoML Systems For Medical Imaging—0
Towards End-to-end Speech-to-text SummarizationCode0
The Creative Frontier of Generative AI: Managing the Novelty-Usefulness Tradeoff—0
Masked Autoencoders are Efficient Continual Federated LearnersCode0
Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera—0
Subgraph Networks Based Contrastive Learning—0
Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image SegmentationCode0
"A Little is Enough": Few-Shot Quality Estimation based Corpus Filtering improves Machine Translation—0
Joint Pre-training and Local Re-training: Transferable Representation Learning on Multi-source Knowledge GraphsCode0
Input-gradient space particle inference for neural network ensemblesCode0
Multi-View Representation is What You Need for Point-Cloud Pre-Training—0
Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Model—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