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

Showing 25762600 of 10307 papers

TitleStatusHype
Few-Shot Learning for Image Classification of Common FloraCode0
Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer LearningCode0
Cross-project Defect Prediction with An Enhanced Transfer Boosting AlgorithmCode0
Few-Shot Fruit Segmentation via Transfer LearningCode0
Deep Categorization with Semi-Supervised Self-Organizing MapsCode0
Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learningCode0
Attentive Multi-Task Deep Reinforcement LearningCode0
Few-shot classification in Named Entity Recognition TaskCode0
Few-Shot Image Recognition With Knowledge TransferCode0
Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methodsCode0
Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer ModelsCode0
Cross-Modal Transfer from Memes to Videos: Addressing Data Scarcity in Hateful Video DetectionCode0
Deep Convolutional Neural Networks for Palm Fruit Maturity ClassificationCode0
Deep Convolution Networks for Compression Artifacts ReductionCode0
Feudal Graph Reinforcement LearningCode0
Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual LearningCode0
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
Fine-Grained Classification for Poisonous Fungi Identification with Transfer LearningCode0
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain RecommendationCode0
Federated Machine Learning: Concept and ApplicationsCode0
Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things ApplicationCode0
Improved Training for 3D Point Cloud ClassificationCode0
Improved transferability of self-supervised learning models through batch normalization finetuningCode0
Cross Modality Knowledge Distillation for Multi-Modal Aerial View Object ClassificationCode0
Federated Domain Generalization via Prompt Learning and AggregationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2Unverified
2DFA-ENTAccuracy69.2Unverified
3DFA-SAFNAccuracy69.1Unverified
4EasyTLAccuracy63.3Unverified
5MEDAAccuracy60.3Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95Unverified