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 6076–6100 of 10307 papers

TitleStatusHype
Artificial Intelligence for COVID-19 Detection -- A state-of-the-art review—0
Artificial Bee Colony optimization of Deep Convolutional Neural Networks in the context of Biomedical Imaging—0
Hierarchical Adaptive Expert for Multimodal Sentiment Analysis—0
Self-Supervised Knowledge Transfer via Loosely Supervised Auxiliary Tasks—0
Hierarchical Cross-Modality Knowledge Transfer with Sinkhorn Attention for CTC-based ASR—0
Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals—0
Hierarchical Granularity Transfer Learning—0
Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime—0
Hierarchical Neural Network Approaches for Long Document Classification—0
Hierarchical Optimal Transport for Comparing Histopathology Datasets—0
Hierarchical Recurrent Aggregative Generation for Few-Shot NLG—0
Hierarchical Recurrent Aggregative Generation for Few-Shot NLG—0
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision—0
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision—0
Hierarchical Side-Tuning for Vision Transformers—0
Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures—0
Hierarchical Transfer Learning for Multi-label Text Classification—0
Self-Supervised Learning Featuring Small-Scale Image Dataset for Treatable Retinal Diseases Classification—0
A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy—0
Exploring Optimal Deep Learning Models for Image-based Malware Variant Classification—0
MERL: Multi-Head Reinforcement Learning—0
Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changes—0
High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks—0
A Robust Illumination-Invariant Camera System for Agricultural Applications—0
High-Order Deep Meta-Learning with Category-Theoretic Interpretation—0
Show:102550
← PrevPage 244 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