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 4951–4975 of 10307 papers

TitleStatusHype
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes—0
Polish Medical Exams: A new dataset for cross-lingual medical knowledge transfer assessment—0
Polite Task-oriented Dialog Agents: To Generate or to Rewrite?—0
Polymer Informatics with Multi-Task Learning—0
POMDP-based dialogue manager adaptation to extended domains—0
POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation—0
Pose-Guided Knowledge Transfer for Object Part Segmentation—0
Positional Attention-based Frame Identification with BERT: A Deep Learning Approach to Target Disambiguation and Semantic Frame Selection—0
Post-Earthquake Assessment of Buildings Using Deep Learning—0
POSTECH Submission on Duolingo Shared Task—0
POSTER: Diagnosis of COVID-19 through Transfer Learning Techniques on CT Scans: A Comparison of Deep Learning Models—0
Post-Transfer Learning Statistical Inference in High-Dimensional Regression—0
Posture recognition using an RGB-D camera : exploring 3D body modeling and deep learning approaches—0
PotatoPestNet: A CTInceptionV3-RS-Based Neural Network for Accurate Identification of Potato Pests—0
Power and Modulation Format Transfer Learning for Neural Network Equalizers in Coherent Optical Transmission Systems—0
Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks—0
PowerGraph: Using neural networks and principal components to determine multivariate statistical power trade-offs—0
PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram—0
PP-LinkNet: Improving Semantic Segmentation of High Resolution Satellite Imagery with Multi-stage Training—0
Practical and sample efficient zero-shot HPO—0
Practical Insights into Knowledge Distillation for Pre-Trained Models—0
Practical Semantic Parsing for Spoken Language Understanding—0
Practical Transferability Estimation for Image Classification Tasks—0
PRADO: Projection Attention Networks for Document Classification On-Device—0
PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency—0
Show:102550
← PrevPage 199 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