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

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Showing 6751–6775 of 10307 papers

TitleStatusHype
A Novel Method For Designing Transferable Soft Sensors And Its Application—0
The Power of Contrast for Feature Learning: A Theoretical Analysis—0
Knowledge Transfer Between Artificial Intelligence Systems—0
Knowledge transfer between bridges for drive-by monitoring using adversarial and multi-task learning—0
Knowledge Transfer between Buildings for Seismic Damage Diagnosis through Adversarial Learning—0
Knowledge Transfer between Datasets for Learning-based Tissue Microstructure Estimation—0
Knowledge transfer between speakers for personalised dialogue management—0
Knowledge Transfer between Structured and Unstructured Sources for Complex Question Answering—0
Knowledge Transfer by Discriminative Pre-training for Academic Performance Prediction—0
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis—0
Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review—0
Knowledge Transfer for Dynamic Multi-objective Optimization with a Changing Number of Objectives—0
Knowledge Transfer for Efficient On-device False Trigger Mitigation—0
Knowledge Transfer for Few-shot Segmentation of Novel White Matter Tracts—0
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU—0
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning—0
Text-Driven Image Manipulation via Semantic-Aware Knowledge Transfer—0
Knowledge Transfer for Pseudo-code Generation from Low Resource Programming Language—0
Knowledge Transfer for Scene-specific Motion Prediction—0
Knowledge transfer for surgical activity prediction—0
Knowledge Transfer from Answer Ranking to Answer Generation—0
Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs—0
Knowledge Transfer from Large-scale Pretrained Language Models to End-to-end Speech Recognizers—0
A Novel Method for Accurate & Real-time Food Classification: The Synergistic Integration of EfficientNetB7, CBAM, Transfer Learning, and Data Augmentation—0
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning—0
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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