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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3551–3575 of 10307 papers

TitleStatusHype
2M-NER: Contrastive Learning for Multilingual and Multimodal NER with Language and Modal Fusion—0
An Explainable Vision Transformer with Transfer Learning Combined with Support Vector Machine Based Efficient Drought Stress Identification—0
Compiler Provenance Recovery for Multi-CPU Architectures Using a Centrifuge Mechanism—0
Comparison of Transfer Learning based Additive Manufacturing Models via A Case Study—0
Comparison of semi-supervised learning methods for High Content Screening quality control—0
Monocular Cyclist Detection with Convolutional Neural Networks—0
Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection—0
Comparison of self-supervised in-domain and supervised out-domain transfer learning for bird species recognition—0
A Semi-supervised Approach to Generate the Code-Mixed Text using Pre-trained Encoder and Transfer Learning—0
Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection—0
Comparison of fine-tuning strategies for transfer learning in medical image classification—0
A Semiparametric Efficient Approach To Label Shift Estimation and Quantification—0
A Game-Theoretic Perspective of Generalization in Reinforcement Learning—0
Comparison of different CNNs for breast tumor classification from ultrasound images—0
Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification—0
A Semantics-Guided Class Imbalance Learning Model for Zero-Shot Classification—0
Comparing Unsupervised Word Translation Methods Step by Step—0
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark—0
A Self-attention Knowledge Domain Adaptation Network for Commercial Lithium-ion Batteries State-of-health Estimation under Shallow Cycles—0
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning—0
Comparing Male Nyala and Male Kudu Classification using Transfer Learning with ResNet-50 and VGG-16—0
A Segmentation Foundation Model for Diverse-type Tumors—0
Comparative Evaluation of Transfer Learning for Classification of Brain Tumor Using MRI—0
A Seed-Augment-Train Framework for Universal Digit Classification—0
Adam Mickiewicz University’s English-Hausa Submissions to the WMT 2021 News Translation Task—0
Show:102550
← PrevPage 143 of 413Next →
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