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 9376–9400 of 10307 papers

TitleStatusHype
Conservation AI: Live Stream Analysis for the Detection of Endangered Species Using Convolutional Neural Networks and Drone Technology—0
Considerations for a PAP Smear Image Analysis System with CNN Features—0
Binary Classification of Alzheimer Disease using sMRI Imaging modality and Deep Learning—0
Considering Race a Problem of Transfer Learning—0
Consistency and Diversity induced Human Motion Segmentation—0
Bilingual Transfer Learning for Online Product Classification—0
READ: Recurrent Adaptation of Large Transformers—0
Constrained Deep Transfer Feature Learning and its Applications—0
Constraining Latent Space to Improve Deep Self-Supervised e-Commerce Products Embeddings for Downstream Tasks—0
Bilingual Language Modeling, A transfer learning technique for Roman Urdu—0
Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese—0
Contact Area Detector using Cross View Projection Consistency for COVID-19 Projects—0
Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning—0
Realized Volatility Forecasting for New Issues and Spin-Offs using Multi-Source Transfer Learning—0
Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition—0
Context-aware Domain Adaptation for Time Series Anomaly Detection—0
Context-Aware Policy Reuse—0
Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation—0
Real-Time And Robust 3D Object Detection with Roadside LiDARs—0
Context-Aware Text Normalisation for Historical Dialects—0
Context-driven Visual Object Recognition based on Knowledge Graphs—0
The LMU Munich System for the WMT20 Very Low Resource Supervised MT Task—0
Context-PEFT: Efficient Multi-Modal, Multi-Task Fine-Tuning—0
Bilinear classifiers for visual recognition—0
Real-time Detection of 2D Tool Landmarks with Synthetic Training Data—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