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 3351–3375 of 10307 papers

TitleStatusHype
Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning—0
Efficient Architecture Search for Continual Learning—0
Efficient Argument Structure Extraction with Transfer Learning and Active Learning—0
Efficient Audio Captioning Transformer with Patchout and Text Guidance—0
Evaluation of taxonomic and neural embedding methods for calculating semantic similarity—0
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data—0
A Novel Method For Designing Transferable Soft Sensors And Its Application—0
An adaptive human-in-the-loop approach to emission detection of Additive Manufacturing processes and active learning with computer vision—0
Deep Image Category Discovery using a Transferred Similarity Function—0
Efficient Continual Adaptation of Pretrained Robotic Policy with Online Meta-Learned Adapters—0
Estimating Bicycle Route Attractivity from Image Data—0
A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations—0
Automatic lesion segmentation and Pathological Myopia classification in fundus images—0
ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning—0
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface—0
Efficient Deployment of Deep MIMO Detection Using Learngene—0
Efficient Discrete Physics-informed Neural Networks for Addressing Evolutionary Partial Differential Equations—0
Efficient Domain Adaptation for Speech Foundation Models—0
Case Study of Model Adaptation: Transfer Learning and Online Learning—0
Efficient Expansion and Gradient Based Task Inference for Replay Free Incremental Learning—0
Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation—0
Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective—0
Automatic Labeling of Data for Transfer Learning—0
DeepGaze II: Reading fixations from deep features trained on object recognition—0
DeepGamble: Towards unlocking real-time player intelligence using multi-layer instance segmentation and attribute detection—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