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 2451–2475 of 10307 papers

TitleStatusHype
Don't Just Pay Attention, PLANT It: Transfer L2R Models to Fine-tune Attention in Extreme Multi-Label Text Classification—0
Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging—0
Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning—0
A Big Data-empowered System for Real-time Detection of Regional Discriminatory Comments on Vietnamese Social Media—0
Breast Cancer Histopathology Classification using CBAM-EfficientNetV2 with Transfer Learning—0
Towards Neural-Network-based optical temperature sensing of Semiconductor Membrane External Cavity Laser—0
The PV-ALE Dataset: Enhancing Apple Leaf Disease Classification Through Transfer Learning with Convolutional Neural Networks—0
Yoga Pose Classification Using Transfer Learning—0
Multi-modal Speech Emotion Recognition via Feature Distribution Adaptation NetworkCode0
Not All Languages are Equal: Insights into Multilingual Retrieval-Augmented GenerationCode0
Collaborative Knowledge Fusion: A Novel Approach for Multi-task Recommender Systems via LLMs—0
KANsformer for Scalable Beamforming—0
Breccia and basalt classification of thin sections of Apollo rocks with deep learningCode0
Adaptive Transfer Clustering: A Unified FrameworkCode0
Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support—0
Uncovering Capabilities of Model Pruning in Graph Contrastive Learning—0
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning—0
Detection-Guided Deep Learning-Based Model with Spatial Regularization for Lung Nodule Segmentation—0
Sensor2Text: Enabling Natural Language Interactions for Daily Activity Tracking Using Wearable Sensors—0
Pseudo-Label Enhanced Prototypical Contrastive Learning for Uniformed Intent DiscoveryCode0
A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection—0
Learning the Regularization Strength for Deep Fine-Tuning via a Data-Emphasized Variational ObjectiveCode0
Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models—0
Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis—0
Gene-Metabolite Association Prediction with Interactive Knowledge Transfer Enhanced Graph for Metabolite Production—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