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 7176–7200 of 10307 papers

TitleStatusHype
A New Channel Boosted Convolutional Neural Network using Transfer Learning—0
How to improve CNN-based 6-DoF camera pose estimation—0
Leveraging universality of jet taggers through transfer learning—0
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems—0
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-Modal Knowledge Transfer—0
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer—0
Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models—0
Size doesn't matter: predicting physico- or biochemical properties based on dozens of molecules—0
An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition—0
LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network—0
An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing—0
Lifelong Event Detection with Embedding Space Separation and Compaction—0
An evaluation of pre-trained models for feature extraction in image classification—0
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems—0
Size Independent Neural Transfer for RDDL Planning—0
Lifelong Learning for Fog Load Balancing: A Transfer Learning Approach—0
A Comparative Study of Transfer Learning for Emotion Recognition using CNN and Modified VGG16 Models—0
Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer—0
Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition and Selective Transfer—0
Lifelong Machine Learning for Topic Modeling and Beyond—0
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora—0
Lifelong Reinforcement Learning with Similarity-Driven Weighting by Large Models—0
Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward Machines—0
Lifelong Sequence Generation with Dynamic Module Expansion and Adaptation—0
LIFT-SLAM: a deep-learning feature-based monocular visual SLAM method—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