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 8851–8900 of 10307 papers

TitleStatusHype
CRL: Class Representative Learning for Image Classification—0
CRNNTL: convolutional recurrent neural network and transfer learning for QSAR modelling—0
CromSS: Cross-modal pre-training with noisy labels for remote sensing image segmentation—0
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer—0
Cross-Architecture Transfer Learning for Linear-Cost Inference Transformers—0
Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals—0
Cross-City Transfer Learning for Deep Spatio-Temporal Prediction—0
Cross-Cultural Transfer Learning for Chinese Offensive Language Detection—0
Cross-Cultural Transfer Learning for Text Classification—0
Cross-database non-frontal facial expression recognition based on transductive deep transfer learning—0
Cross Dataset Analysis and Network Architecture Repair for Autonomous Car Lane Detection—0
Cross-Dataset Experimental Study of Radar-Camera Fusion in Bird's-Eye View—0
Cross-domain Activity Recognition via Substructural Optimal Transport—0
Cross Domain Adaptation by Learning Partially Shared Classifiers and Weighting Source Data Points in the Shared Subspaces—0
Cross-domain aspect extraction for sentiment analysis: a transductive learning approach—0
Cross-domain attribute representation based on convolutional neural network—0
Cross-domain Augmentation Networks for Click-Through Rate Prediction—0
Cross-domain Deep Feature Combination for Bird Species Classification with Audio-visual Data—0
Cross Domain Emotion Recognition using Few Shot Knowledge Transfer—0
Cross-domain Feature Selection for Language Identification—0
Cross-Domain Few-Shot Learning with Meta Fine-Tuning—0
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation—0
Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data—0
Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition—0
Cross Domain Knowledge Transfer for Unsupervised Vehicle Re-identification—0
Cross Domain Knowledge Transfer for Person Re-identification—0
Cross-Domain Label Propagation for Domain Adaptation with Discriminative Graph Self-Learning—0
Cross-Domain Latent Modulation for Variational Transfer Learning—0
Cross-domain Microscopy Cell Counting by Disentangled Transfer Learning—0
Cross-domain Named Entity Recognition via Graph Matching—0
Cross-domain Network Representations—0
Cross-Domain Open-Set Machinery Fault Diagnosis Based on Adversarial Network With Multiple Auxiliary Classifiers—0
Cross-Domain Recommendation via Preference Propagation GraphNet—0
Cross-domain Recommender Systems via Multimodal Domain Adaptation—0
Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators—0
Cross-Domain Robustness of Transformer-based Keyphrase Generation—0
Cross-domain Text Classification with Multiple Domains and Disparate Label Sets—0
Cross-Domain Training for Goal-Oriented Conversational Agents—0
Cross-Domain Transfer in Reinforcement Learning using Target Apprentice—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
Cross-Language Transfer Learning using Visual Information for Automatic Sign Gesture Recognition—0
Show:102550
← PrevPage 178 of 207Next →
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