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 2626–2650 of 10307 papers

TitleStatusHype
Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time seriesCode0
Combining Behaviors with the Successor Features Keyboard—0
Burgers' pinns with implicit euler transfer learning—0
Quantum Federated Learning With Quantum Networks—0
Unlocking the Transferability of Tokens in Deep Models for Tabular Data—0
DREAM+: Efficient Dataset Distillation by Bidirectional Representative MatchingCode1
Bayesian Active Learning in the Presence of Nuisance Parameters—0
Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition—0
Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning—0
Minimax Optimal Transfer Learning for Kernel-based Nonparametric Regression—0
On the Transferability of Visually Grounded PCFGsCode1
Ladder Bottom-up Convolutional Bidirectional Variational Autoencoder for Image Translation of Dotted Arabic Expiration Dates—0
Foundation Model's Embedded Representations May Detect Distribution Shift—0
A Novel Transfer Learning Method Utilizing Acoustic and Vibration Signals for Rotating Machinery Fault Diagnosis—0
Using Human-like Mechanism to Weaken Effect of Pre-training Weight Bias in Face-Recognition Convolutional Neural Network—0
Diagnosis-oriented Medical Image Compression with Efficient Transfer Learning—0
The Less the Merrier? Investigating Language Representation in Multilingual Models—0
Representation Learning via Consistent Assignment of Views over Random PartitionsCode0
Streamlining Brain Tumor Classification with Custom Transfer Learning in MRI Images—0
Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork PromptCode0
Unsupervised Representation Learning to Aid Semi-Supervised Meta LearningCode0
Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual GeneralizationCode0
Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal MechanismsCode0
Enhancing High-Resolution 3D Generation through Pixel-wise Gradient ClippingCode1
Getting aligned on representational alignmentCode0
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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