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 10001–10025 of 10307 papers

TitleStatusHype
Policy Transfer with Strategy OptimizationCode0
Deep Manifold Learning for Reading Comprehension and Logical Reasoning Tasks with Polytuplet LossCode0
Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual TransferCode0
PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-IdentificationCode0
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera RelocalizationCode0
POS-tagging to highlight the skeletal structure of sentencesCode0
Post-Hoc Domain Adaptation via Guided Data HomogenizationCode0
Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge TransferCode0
Practical Deep Learning for Cloud, Mobile, and EdgeCode0
Practical multi-fidelity machine learning: fusion of deterministic and Bayesian modelsCode0
Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural NetworksCode0
Towards generalization of drug response prediction to single cells and patients utilizing importance-aware multi-source domain transfer learningCode0
Predicting Stroke through Retinal Graphs and Multimodal Self-supervised LearningCode0
Predicting the Location of Bicycle-sharing Stations using OpenStreetMap DataCode0
Evaluation of Activated Sludge Settling Characteristics from Microscopy Images with Deep Convolutional Neural Networks and Transfer LearningCode0
Pre-Finetuning for Few-Shot Emotional Speech RecognitionCode0
Preserving Fine-Grain Feature Information in Classification via Entropic RegularizationCode0
Pretrained audio neural networks for Speech emotion recognition in PortugueseCode0
Pre-Trained Language-Meaning Models for Multilingual Parsing and GenerationCode0
Injecting structural hints: Using language models to study inductive biases in language learningCode0
PRIBOOT: A New Data-Driven Expert for Improved Driving SimulationsCode0
Can Synthetic Faces Undo the Damage of Dataset Bias to Face Recognition and Facial Landmark Detection?Code0
Prior-based Objective Inference Mining Potential Uncertainty for Facial Expression RecognitionCode0
Privacy-Enhanced Zero-Shot Learning via Data-Free Knowledge TransferCode0
Privacy-Preserving CNN Training with Transfer Learning: Multiclass Logistic RegressionCode0
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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