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 1076–1100 of 10307 papers

TitleStatusHype
COLA: Cross-city Mobility Transformer for Human Trajectory SimulationCode1
New Protocols and Negative Results for Textual Entailment Data CollectionCode1
Domain Adaptation with Auxiliary Target Domain-Oriented ClassifierCode1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
Comparative Evaluation of Pretrained Transfer Learning Models on Automatic Short Answer GradingCode1
Composable Sparse Fine-Tuning for Cross-Lingual TransferCode1
A Recent Survey of Heterogeneous Transfer LearningCode1
AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an AppCode1
Compressing BERT: Studying the Effects of Weight Pruning on Transfer LearningCode1
Adapting LLaMA Decoder to Vision TransformerCode1
Broken Neural Scaling LawsCode1
DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modelingCode1
Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation?Code1
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot FillingCode1
Position: Considerations for Differentially Private Learning with Large-Scale Public PretrainingCode1
AgileGAN: stylizing portraits by inversion-consistent transfer learningCode1
Bridging Anaphora Resolution as Question AnsweringCode1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional NetworkCode1
MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and ResolutionCode1
Asymmetric metric learning for knowledge transferCode1
Continual Sequence Generation with Adaptive Compositional ModulesCode1
Contour Knowledge Transfer for Salient Object DetectionCode1
Contrastive Embeddings for Neural ArchitecturesCode1
Breaking the Data Barrier -- Building GUI Agents Through Task GeneralizationCode1
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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