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 5901–5925 of 10307 papers

TitleStatusHype
Selective transfer learning with adversarial training for stock movement prediction—0
GLoMo: Unsupervised Learning of Transferable Relational Graphs—0
Selective Transfer with Reinforced Transfer Network for Partial Domain Adaptation—0
Assistive Diagnostic Tool for Brain Tumor Detection using Computer Vision—0
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data—0
GMP-TL: Gender-augmented Multi-scale Pseudo-label Enhanced Transfer Learning for Speech Emotion Recognition—0
GNN-based Precoder Design and Fine-tuning for Cell-free Massive MIMO with Real-world CSI—0
Assistive Completion of Agrammatic Aphasic Sentences: A Transfer Learning Approach using Neurolinguistics-based Synthetic Dataset—0
Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer Learning—0
Domain Adaptation with Joint Learning for Generic, Optical Car Part Recognition and Detection Systems (Go-CaRD)—0
Assessment of deep learning based blood pressure prediction from PPG and rPPG signals—0
Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks—0
Self-adaptive Multi-task Particle Swarm Optimization—0
Assessing the Value of Transfer Learning Metrics for RF Domain Adaptation—0
Good Neighbors Are All You Need for Chinese Grapheme-to-Phoneme Conversion—0
GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation—0
Good View Hunting: Learning Photo Composition From Dense View Pairs—0
Google is all you need: Semi-Supervised Transfer Learning Strategy For Light Multimodal Multi-Task Classification Model—0
Assessing the Portability of Parameter Matrices Trained by Parameter-Efficient Finetuning Methods—0
Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images—0
Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images—0
Self and Mixed Supervision to Improve Training Labels for Multi-Class Medical Image Segmentation—0
Self-Calibrating Indoor Localization with Crowdsourcing Fingerprints and Transfer Learning—0
Assessing Pre-trained Models for Transfer Learning through Distribution of Spectral Components—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—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