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 7276–7300 of 10307 papers

TitleStatusHype
Empirical Study on the Software Engineering Practices in Open Source ML Package Repositories—0
Longitudinal detection of new MS lesions using Deep Learning—0
An Empirical Study on Measuring the Similarity of Sentential Arguments with Language Model Domain Adaptation—0
An Empirical Study on Cross-Lingual and Cross-Domain Transfer for Legal Judgment Prediction—0
Long-tail Recognition via Compositional Knowledge Transfer—0
Data-Efficient Pretraining via Contrastive Self-Supervision—0
Long-Term Ensemble Learning of Visual Place Classifiers—0
Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks—0
SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography—0
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling—0
Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition—0
Looks Like Magic: Transfer Learning in GANs to Generate New Card Illustrations—0
LORM: Learning to Optimize for Resource Management in Wireless Networks with Few Training Samples—0
The PV-ALE Dataset: Enhancing Apple Leaf Disease Classification Through Transfer Learning with Convolutional Neural Networks—0
An Empirical Study of Scaling Laws for Transfer—0
LoSA: Long-Short-range Adapter for Scaling End-to-End Temporal Action Localization—0
LOTUS: Continual Imitation Learning for Robot Manipulation Through Unsupervised Skill Discovery—0
Love Thy Neighbor: Combining Two Neighboring Low-Resource Languages for Translation—0
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models—0
Low-Complexity Inference in Continual Learning via Compressed Knowledge Transfer—0
A Comparative Analysis Towards Melanoma Classification Using Transfer Learning by Analyzing Dermoscopic Images—0
TheraGen: Therapy for Every Generation—0
Low-Fidelity Video Encoder Optimization for Temporal Action Localization—0
MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles—0
Low Latency Real-Time Seizure Detection Using Transfer Deep Learning—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