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 7151–7175 of 10307 papers

TitleStatusHype
Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN Outspeeds Data Augmentation by Smaller Sample -- Application in Gomoku Reinforcement Learning—0
Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization—0
SWP-LeafNET: A novel multistage approach for plant leaf identification based on deep CNN—0
Symbiotic Message Passing Model for Transfer Learning between Anti-Fungal and Anti-Bacterial Domains—0
Symbol Correctness in Deep Neural Networks Containing Symbolic Layers—0
Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes—0
Synergistic Fusion of Multi-Source Knowledge via Evidence Theory for High-Entropy Alloy Discovery—0
Building Advanced Dialogue Managers for Goal-Oriented Dialogue Systems—0
SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation—0
Synonym Expansion for Large Shopping Taxonomies—0
Synonyms, Antonyms and Beyond—0
Syntactically Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction—0
Syntax-based Transfer Learning for the Task of Biomedical Relation Extraction—0
Building and Road Segmentation Using EffUNet and Transfer Learning Approach—0
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation—0
Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification—0
Synthetic Image Data for Deep Learning—0
Building a Question and Answer System for News Domain—0
Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach—0
System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications—0
System Demo for Transfer Learning from Vision to Language using Domain Specific CNN Accelerator for On-Device NLP Applications—0
T3: A Novel Zero-shot Transfer Learning Framework Iteratively Training on an Assistant Task for a Target Task—0
Building Efficient Lightweight CNN Models—0
Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using Deep Learning with Visual Representations—0
TabKAN: Advancing Tabular Data Analysis using Kolmogorov-Arnold Network—0
Show:102550
← PrevPage 287 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