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 9501–9525 of 10307 papers

TitleStatusHype
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment—0
Cooperative Self-training of Machine Reading Comprehension—0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
COPD Classification in CT Images Using a 3D Convolutional Neural Network—0
Recent Neural Methods on Dialogue State Tracking for Task-Oriented Dialogue Systems: A Survey—0
CopyPaste: An Augmentation Method for Speech Emotion Recognition—0
CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise—0
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey—0
Coregionalised Locomotion Envelopes - A Qualitative Approach—0
rECGnition_v1.0: Arrhythmia detection using cardiologist-inspired multi-modal architecture incorporating demographic attributes in ECG—0
Core Sampling Framework for Pixel Classification—0
The Role of Exploration for Task Transfer in Reinforcement Learning—0
Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique—0
RECLIP: Resource-efficient CLIP by Training with Small Images—0
Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks—0
Recognition of Cardiac MRI Orientation via Deep Neural Networks and a Method to Improve Prediction Accuracy—0
Recognition of Harmful Phytoplankton from Microscopic Images using Deep Learning—0
COSTA: Co-Occurrence Statistics for Zero-Shot Classification—0
Cost-based Feature Transfer for Vehicle Occupant Classification—0
Cost-effective Variational Active Entity Resolution—0
Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation—0
Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration—0
COT: Unsupervised Domain Adaptation With Clustering and Optimal Transport—0
Could Giant Pretrained Image Models Extract Universal Representations?—0
Recognition Of Surface Defects On Steel Sheet 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