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 2526–2550 of 10307 papers

TitleStatusHype
Fine-Grained Classification for Poisonous Fungi Identification with Transfer LearningCode0
3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic GapCode0
Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant SetupCode0
Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited tripletsCode0
Fine-Grained Emotion Prediction by Modeling Emotion DefinitionsCode0
Few-Shot Image Recognition With Knowledge TransferCode0
Cross-View Policy Learning for Street NavigationCode0
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networksCode0
Growing Neural Network with Shared ParameterCode0
Adaptive Transfer Clustering: A Unified FrameworkCode0
GVdoc: Graph-based Visual Document ClassificationCode0
Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain StudyCode0
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationCode0
Dataset Knowledge Transfer for Class-Incremental Learning without MemoryCode0
DATE: Domain Adaptive Product Seeker for E-commerceCode0
Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learningCode0
Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulationsCode0
Adaptive Meta-Domain Transfer Learning (AMDTL): A Novel Approach for Knowledge Transfer in AICode0
Few-shot classification in Named Entity Recognition TaskCode0
Feudal Graph Reinforcement LearningCode0
A Tulu Resource for Machine TranslationCode0
Few-Shot Fruit Segmentation via Transfer LearningCode0
Debiasing Graph Transfer Learning via Item Semantic Clustering for Cross-Domain RecommendationsCode0
Few-Shot Learning for Image Classification of Common FloraCode0
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain RecommendationCode0
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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