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 9626–9650 of 10307 papers

TitleStatusHype
Rediscovering the Alphabet - On the Innate Universal Grammar—0
Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Prediction—0
Cross-Domain Transfer Learning with CoRTe: Consistent and Reliable Transfer from Black-Box to Lightweight Segmentation Model—0
Cross-domain Transfer of defect features in technical domains based on partial target data—0
Cross-Domain Underwater Image Enhancement Guided by No-Reference Image Quality Assessment: A Transfer Learning Approach—0
Crossed-IoT device portability of Electromagnetic Side Channel Analysis: Challenges and Dataset—0
Cross Encoding as Augmentation: Towards Effective Educational Text Classification—0
Cross-Enhancement Transform Two-Stream 3D ConvNets for Action Recognition—0
Cross-Environment Transfer Learning for Location-Aided Beam Prediction in 5G and Beyond Millimeter-Wave Networks—0
Cross Feature Fusion of Fundus Image and Generated Lesion Map for Referable Diabetic Retinopathy Classification—0
Cross-functional transferability in universal machine learning interatomic potentials—0
Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis—0
Cross-Language Transfer Learning using Visual Information for Automatic Sign Gesture Recognition—0
BeST -- A Novel Source Selection Metric for Transfer Learning—0
BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets—0
Cross-Lingual Transfer Learning for Statistical Type Inference—0
Reduce, Reuse, Recycle: Is Perturbed Data better than Other Language augmentation for Low Resource Self-Supervised Speech Models—0
Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data—0
3D medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in CT images—0
BERT-PersNER: A New Model for Persian Named Entity Recognition—0
Cross-lingual, Character-Level Neural Morphological Tagging—0
Cross-lingual Character-Level Neural Morphological Tagging—0
Cross-lingual Constituency Parsing with Linguistic Typology Knowledge—0
BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews—0
Cross-lingual Universal Dependency Parsing Only from One Monolingual Treebank—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