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 1351–1375 of 10307 papers

TitleStatusHype
Breast-NET: a lightweight DCNN model for breast cancer detection and grading using histological samplesCode0
Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing—0
ECG-FM: An Open Electrocardiogram Foundation ModelCode3
FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across TokenizersCode0
KIF: Knowledge Identification and Fusion for Language Model Continual LearningCode1
GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labeled Data Guide Unlabeled DataCode1
Segmentation of Mental Foramen in Orthopantomographs: A Deep Learning Approach—0
Deep Transfer Learning for Kidney Cancer Diagnosis—0
Model-Based Transfer Learning for Contextual Reinforcement LearningCode1
Non-Causal to Causal SSL-Supported Transfer Learning: Towards a High-Performance Low-Latency Speech Vocoder—0
Anatomical Foundation Models for Brain MRIsCode1
Scaling Law of Sim2Real Transfer Learning in Expanding Computational Materials Databases for Real-World Predictions—0
Online Electric Vehicle Charging Detection Based on Memory-based Transformer using Smart Meter Data—0
Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks—0
Segment Anything in Medical Images and Videos: Benchmark and DeploymentCode7
LLaVA-OneVision: Easy Visual Task TransferCode0
Quantum Transfer Learning for MNIST Classification Using a Hybrid Quantum-Classical Approach—0
Boosting Memory Efficiency in Transfer Learning for High-Resolution Medical Image ClassificationCode1
FE-Adapter: Adapting Image-based Emotion Classifiers to Videos—0
Machine Learning Applications in Medical Prognostics: A Comprehensive Review—0
Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem SolvingCode0
DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric EstimationCode0
Unsupervised Representation Learning by Balanced Self Attention MatchingCode0
AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate DiagnosisCode0
Sharpness-Aware Cross-Domain Recommendation to Cold-Start Users—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