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 7701–7725 of 10307 papers

TitleStatusHype
TOP-GAN: Label-Free Cancer Cell Classification Using Deep Learning with a Small Training Set—0
TopicBERT: A Transformer transfer learning based memory-graph approach for multimodal streaming social media topic detection—0
Topic-driven Distant Supervision Framework for Macro-level Discourse Parsing—0
TOPLight: Lightweight Neural Networks With Task-Oriented Pretraining for Visible-Infrared Recognition—0
TopoCL: Topological Contrastive Learning for Time Series—0
Topological derivative approach for deep neural network architecture adaptation—0
CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification—0
Topological Vanilla Transfer Learning—0
Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process—0
To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of Point Cloud Transfer Learning—0
Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism—0
To Transfer or Not to Transfer: Misclassification Attacks Against Transfer Learned Text Classifiers—0
To transfer or not transfer: Unified transferability metric and analysis—0
To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks—0
Toward a Geometrical Understanding of Self-supervised Contrastive Learning—0
Toward Co-creative Dungeon Generation via Transfer Learning—0
Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models—0
Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search—0
Toward Educator-focused Automated Scoring Systems for Reading and Writing—0
Toward efficient resource utilization at edge nodes in federated learning—0
Toward Efficient Transfer Learning in 6G—0
Toward Fault Detection in Industrial Welding Processes with Deep Learning and Data Augmentation—0
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs—0
Toward Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learning—0
Towards 3D Scene Understanding by Referring Synthetic Models—0
Show:102550
← PrevPage 309 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