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 8451–8475 of 10307 papers

TitleStatusHype
Optimizing Quantum Error Correction Codes with Reinforcement Learning—0
Optimizing Specific and Shared Parameters for Efficient Parameter Tuning—0
Study and development of a Computer-Aided Diagnosis system for classification of chest x-ray images using convolutional neural networks pre-trained for ImageNet and data augmentation—0
Optimizing Two-Pass Cross-Lingual Transfer Learning: Phoneme Recognition and Phoneme to Grapheme Translation—0
Optimizing Vision Transformers with Data-Free Knowledge Transfer—0
Option Compatible Reward Inverse Reinforcement Learning—0
Study of Vision Transformers for Covid-19 Detection from Chest X-rays—0
Optum at MEDIQA 2021: Abstractive Summarization of Radiology Reports using simple BART Finetuning—0
opXRD: Open Experimental Powder X-ray Diffraction Database—0
Order parameters and phase transitions of continual learning in deep neural networks—0
ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs—0
Stuttgart Open Relay Degradation Dataset (SOReDD)—0
Osteosarcoma Tumor Detection using Transfer Learning Models—0
Adversary ML Resilience in Autonomous Driving Through Human Centered Perception Mechanisms—0
Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning—0
Out-of-Task Training for Dialog State Tracking Models—0
Overcome Anterograde Forgetting with Cycled Memory Networks—0
A Brief History of Prompt: Leveraging Language Models. (Through Advanced Prompting)—0
StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models—0
Data augmentation in microscopic images for material data mining—0
Adversary Helps: Gradient-based Device-Free Domain-Independent Gesture Recognition—0
Adversarial Vulnerability of Active Transfer Learning—0
Adversarial Transfer of Pose Estimation Regression—0
Overcoming data scarcity with transfer learning—0
Overcoming Label Ambiguity with Multi-label Iterated Learning—0
Show:102550
← PrevPage 339 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