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 4901–4925 of 10307 papers

TitleStatusHype
Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework—0
Land Use Prediction using Electro-Optical to SAR Few-Shot Transfer Learning—0
FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge—0
Brain Tumor Synthetic Data Generation with Adaptive StyleGANsCode0
Domain-Invariant Feature Alignment Using Variational Inference For Partial Domain Adaptation—0
Fair Generative Models via Transfer LearningCode0
Few-Shot Nested Named Entity Recognition—0
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning—0
PGFed: Personalize Each Client's Global Objective for Federated LearningCode0
Quantum median filter for Total Variation image denoising—0
Coevolutionary Framework for Generalized Multimodal Multi-objective OptimizationCode0
Geometry-Aware Network for Domain Adaptive Semantic Segmentation—0
Video-based Pose-Estimation Data as Source for Transfer Learning in Human Activity Recognition—0
Rethinking Two Consensuses of the Transferability in Deep Learning—0
ClaRet -- A CNN Architecture for Optical Coherence Tomography—0
Progressive Knowledge Transfer Based on Human Visual Perception Mechanism for Perceptual Quality Assessment of Point Clouds—0
Domain Mismatch Doesn't Always Prevent Cross-Lingual Transfer Learning—0
Spatio-Temporal Crop Aggregation for Video Representation Learning—0
From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks—0
Explicit Knowledge Transfer for Weakly-Supervised Code Generation—0
On the Design of Communication-Efficient Federated Learning for Health Monitoring—0
Overlapping oriented imbalanced ensemble learning method based on projective clustering and stagewise hybrid sampling—0
Balanced Semi-Supervised Generative Adversarial Network for Damage Assessment from Low-Data Imbalanced-Class Regime—0
Cross-project Defect Prediction with An Enhanced Transfer Boosting AlgorithmCode0
Data-efficient Modeling of Optical Matrix Multipliers Using Transfer Learning—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