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 2076–2100 of 10307 papers

TitleStatusHype
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces—0
Predicting concentration levels of air pollutants by transfer learning and recurrent neural network—0
Revisiting Projection-based Data Transfer for Cross-Lingual Named Entity Recognition in Low-Resource LanguagesCode0
Distilling Knowledge for Designing Computational Imaging SystemsCode0
Action Recognition Using Temporal Shift Module and Ensemble LearningCode0
Digital Twin Synchronization: Bridging the Sim-RL Agent to a Real-Time Robotic Additive Manufacturing Control—0
LEKA:LLM-Enhanced Knowledge Augmentation—0
Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularization—0
Multimodal Magic Elevating Depression Detection with a Fusion of Text and Audio Intelligence—0
Stiff Transfer Learning for Physics-Informed Neural Networks—0
CoRe-Net: Co-Operational Regressor Network with Progressive Transfer Learning for Blind Radar Signal RestorationCode0
TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models—0
Transfer of Knowledge through Reverse Annealing: A Preliminary Analysis of the Benefits and What to Share—0
Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within z < 1.4 in the Hyper Supreme-Cam Wide SurveyCode0
Cross-Modal Transfer from Memes to Videos: Addressing Data Scarcity in Hateful Video DetectionCode0
Variational Bayesian Adaptive Learning of Deep Latent Variables for Acoustic Knowledge Transfer—0
Building Efficient Lightweight CNN Models—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement LearningCode0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
Explainable YOLO-Based Dyslexia Detection in Synthetic Handwriting Data—0
In-Context Operator Learning for Linear Propagator Models—0
Detection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning—0
Neuronal and structural differentiation in the emergence of abstract rules in hierarchically modulated spiking neural networks—0
Deep Learning-Powered Classification of Thoracic Diseases in Chest X-Rays—0
Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks—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