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 4651–4675 of 10307 papers

TitleStatusHype
Rethinking Efficient Tuning Methods from a Unified Perspective—0
Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis—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
SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS—0
Novel Machine Learning Approach for Predicting Poverty using Temperature and Remote Sensing Data in Ethiopia—0
Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation—0
Human-Inspired Framework to Accelerate Reinforcement LearningCode0
Deep Learning for Identifying Iran's Cultural Heritage Buildings in Need of Conservation Using Image Classification and Grad-CAMCode0
Weighted Sampling for Masked Language Modeling—0
Curriculum Based Multi-Task Learning for Parkinson's Disease Detection—0
On the Use of Power Amplifier Nonlinearity Quotient to Improve Radio Frequency Fingerprint Identification in Time-Varying Channels—0
Cross-lingual Knowledge Transfer via Distillation for Multilingual Information Retrieval—0
CLICKER: Attention-Based Cross-Lingual Commonsense Knowledge Transfer—0
Improving Representational Continuity via Continued PretrainingCode0
Scalable Weight Reparametrization for Efficient Transfer Learning—0
TransferD2: Automated Defect Detection Approach in Smart Manufacturing using Transfer Learning Techniques—0
Choice Fusion as Knowledge for Zero-Shot Dialogue State TrackingCode0
Adapting Pre-trained Language Models for Quantum Natural Language Processing—0
HUST bearing: a practical dataset for ball bearing fault diagnosis—0
Pre-Finetuning for Few-Shot Emotional Speech RecognitionCode0
Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data—0
A Comprehensive Survey on Source-free Domain Adaptation—0
KS-DETR: Knowledge Sharing in Attention Learning for Detection TransformerCode0
Steerable Equivariant Representation 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