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 1001–1025 of 10307 papers

TitleStatusHype
Attention is All You Need to Optimize Wind Farm Operations and Maintenance—0
BioNCERE: Non-Contrastive Enhancement For Relation Extraction In Biomedical Texts—0
Rethinking Inverse Reinforcement Learning: from Data Alignment to Task AlignmentCode0
Mind the Gap: A Generalized Approach for Cross-Modal Embedding Alignment—0
Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks—0
Sequential Order-Robust Mamba for Time Series ForecastingCode0
Nested ResNet: A Vision-Based Method for Detecting the Sensing Area of a Drop-in Gamma Probe—0
MutaPLM: Protein Language Modeling for Mutation Explanation and EngineeringCode4
Don't Just Pay Attention, PLANT It: Transfer L2R Models to Fine-tune Attention in Extreme Multi-Label Text Classification—0
Transfer Learning in Vocal Education: Technical Evaluation of Limited Samples Describing Mezzo-soprano—0
Self-Driving Car Racing: Application of Deep Reinforcement Learning—0
A Big Data-empowered System for Real-time Detection of Regional Discriminatory Comments on Vietnamese Social Media—0
Yoga Pose Classification Using Transfer Learning—0
Breast Cancer Histopathology Classification using CBAM-EfficientNetV2 with Transfer Learning—0
The PV-ALE Dataset: Enhancing Apple Leaf Disease Classification Through Transfer Learning with Convolutional Neural Networks—0
Towards Neural-Network-based optical temperature sensing of Semiconductor Membrane External Cavity Laser—0
Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning—0
Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging—0
Not All Languages are Equal: Insights into Multilingual Retrieval-Augmented GenerationCode0
Multi-modal Speech Emotion Recognition via Feature Distribution Adaptation NetworkCode0
KANsformer for Scalable Beamforming—0
Breccia and basalt classification of thin sections of Apollo rocks with deep learningCode0
Adaptive Transfer Clustering: A Unified FrameworkCode0
Collaborative Knowledge Fusion: A Novel Approach for Multi-task Recommender Systems via LLMs—0
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-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