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 8076–8100 of 10307 papers

TitleStatusHype
Why is the video analytics accuracy fluctuating, and what can we do about it?—0
WiFiNet: WiFi-based indoor localisation using CNNs—0
WikiDBGraph: Large-Scale Database Graph of Wikidata for Collaborative Learning—0
Wiki to Automotive: Understanding the Distribution Shift and its impact on Named Entity Recognition—0
Wildfire Detection Via Transfer Learning: A Survey—0
Will This Idea Spread Beyond Academia? Understanding Knowledge Transfer of Scientific Concepts across Text Corpora—0
Window Detection In Facade Imagery: A Deep Learning Approach Using Mask R-CNN—0
Winning the ICCV'2021 VALUE Challenge: Task-aware Ensemble and Transfer Learning with Visual Concepts—0
Wireless Channel Aware Data Augmentation Methods for Deep Learning-Based Indoor Localization—0
Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?—0
Wisdom of Committee: Distilling from Foundation Model to Specialized Application Model—0
Within the Lack of COVID-19 Benchmark Dataset: A Novel GAN with Deep Transfer Learning for Corona-virus Detection in Chest X-ray Images—0
WLV-RIT at HASOC-Dravidian-CodeMix-FIRE2020: Offensive Language Identification in Code-switched YouTube Comments—0
WMAdapter: Adding WaterMark Control to Latent Diffusion Models—0
WordNet2Vec: Corpora Agnostic Word Vectorization Method—0
Word Order Typology through Multilingual Word Alignment—0
Wound Severity Classification using Deep Neural Network—0
Write a Classifier: Predicting Visual Classifiers from Unstructured Text—0
WUT at SemEval-2019 Task 9: Domain-Adversarial Neural Networks for Domain Adaptation in Suggestion Mining—0
X2CT-CLIP: Enable Multi-Abnormality Detection in Computed Tomography from Chest Radiography via Tri-Modal Contrastive Learning—0
Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis—0
XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection—0
Successor Feature Representations—0
XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP—0
XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering—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