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 3626–3650 of 10307 papers

TitleStatusHype
Optimal transfer protocol by incremental layer defrosting—0
Distillation from Heterogeneous Models for Top-K RecommendationCode1
Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice ModelsCode0
Heterogeneous Graph Contrastive Learning for RecommendationCode1
Multi-Task Self-Supervised Time-Series Representation Learning—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning—0
Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density PilotsCode1
Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis—0
Rethinking Efficient Tuning Methods from a Unified Perspective—0
Speeding Up EfficientNet: Selecting Update Blocks of Convolutional Neural Networks using Genetic Algorithm in Transfer Learning—0
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization—0
Human-Inspired Framework to Accelerate Reinforcement LearningCode0
Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation—0
Novel Machine Learning Approach for Predicting Poverty using Temperature and Remote Sensing Data in Ethiopia—0
Weighted Sampling for Masked Language Modeling—0
Deep Learning for Identifying Iran's Cultural Heritage Buildings in Need of Conservation Using Image Classification and Grad-CAMCode0
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositionsCode1
SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS—0
On the Use of Power Amplifier Nonlinearity Quotient to Improve Radio Frequency Fingerprint Identification in Time-Varying Channels—0
You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link PredictionCode1
The Role of Pre-training Data in Transfer LearningCode1
Curriculum Based Multi-Task Learning for Parkinson's Disease Detection—0
CLICKER: Attention-Based Cross-Lingual Commonsense Knowledge Transfer—0
Cross-lingual Knowledge Transfer via Distillation for Multilingual Information Retrieval—0
Scalable Weight Reparametrization for Efficient 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