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 9226–9250 of 10307 papers

TitleStatusHype
DKC: Differentiated Knowledge Consolidation for Cloth-Hybrid Lifelong Person Re-identificationCode0
DLDNN: Deterministic Lateral Displacement Design Automation by Neural NetworksCode0
DocFace: Matching ID Document Photos to SelfiesCode0
DocLangID: Improving Few-Shot Training to Identify the Language of Historical DocumentsCode0
Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural NetworksCode0
Does language help generalization in vision models?Code0
Does Rationale Quality Matter? Enhancing Mental Disorder Detection via Selective Reasoning DistillationCode0
Domain2Vec: Domain Embedding for Unsupervised Domain AdaptationCode0
Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite ImageryCode0
Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word EmbeddingCode0
Domain Adaptation for MRI Organ Segmentation using Reverse Classification AccuracyCode0
Low-Rank Subspace Override for Unsupervised Domain AdaptationCode0
Domain Adaptation via Maximizing Surrogate Mutual InformationCode0
Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text SummarizationCode0
Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text SummarizationCode0
Domain Adaptation with Randomized Expectation MaximizationCode0
Domain Adaptive Person Re-Identification via Coupling OptimizationCode0
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware DetectionCode0
Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out TrainingCode0
Domain Generalization by Marginal Transfer LearningCode0
Do Neural Ranking Models Intensify Gender Bias?Code0
Don't Just Scratch the Surface: Enhancing Word Representations for Korean with HanjaCode0
Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model EmulationCode0
DRCD: a Chinese Machine Reading Comprehension DatasetCode0
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversionCode0
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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