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Transfer-Learning-Based Online Mura Defect Classification

https://doi.org/10.1109/tsm.2017.2777499
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Extreme learning machine: Theory and applications
inferredcites →https://doi.org/10.1016/j.patcog.2005.07.007openalex-snapshot
A mura detection method
inferredcites →https://doi.org/10.1016/j.patrec.2010.07.013openalex-snapshot
Automatic detection of Mura defect in TFT-LCD based on regression diagnostics
inferredcites →https://doi.org/10.1080/15599610701548787openalex-snapshot
Zone-Based Inspection and Defect Classification for LCD Manufacturing:Trivial Defect Free Procedure for TFT Glass Inspection
inferredcites →https://doi.org/10.1109/cvpr.2014.222openalex-snapshot
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvprw.2014.131openalex-snapshot
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
inferredcites →https://doi.org/10.1109/icassp.2012.6288864openalex-snapshot
Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition
inferredcites →https://doi.org/10.1109/ijcnn.2004.1380068openalex-snapshot
Extreme learning machine: a new learning scheme of feedforward neural networks
inferredcites →https://doi.org/10.1109/ijcnn.2015.7280683openalex-snapshot
Deep convolutional neural networks as generic feature extractors
inferredcites →https://doi.org/10.1109/isie.2009.5213760openalex-snapshot
Automatic defect classification of TFT-LCD panels using machine learning
inferredcites →https://doi.org/10.1109/taslp.2015.2438544openalex-snapshot
Data Augmentation for Deep Neural Network Acoustic Modeling
inferredcites →https://doi.org/10.1109/tcsii.2005.857540openalex-snapshot
Can threshold networks be trained directly?
inferredcites →https://doi.org/10.1109/tepm.2008.926117openalex-snapshot
A Neural-Network Approach for Defect Recognition in TFT-LCD Photolithography Process
inferredcites →https://doi.org/10.1109/tkde.2006.94openalex-snapshot
Efficient classification across multiple database relations: a CrossMine approach
inferredcites →https://doi.org/10.1109/tkde.2007.190677openalex-snapshot
A Lazy Approach to Associative Classification
inferredcites →https://doi.org/10.1109/tkde.2009.191openalex-snapshot
A Survey on Transfer Learning
inferredcites →https://doi.org/10.1109/tnn.2006.875974openalex-snapshot
Real-Time Learning Capability of Neural Networks
inferredcites →https://doi.org/10.1109/tnn.2006.880583openalex-snapshot
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
inferredcites →https://doi.org/10.1109/tpami.2012.59openalex-snapshot
3D Convolutional Neural Networks for Human Action Recognition
inferredcites →https://doi.org/10.1109/tsm.2017.2648856openalex-snapshot
Unsupervised-Learning-Based Feature-Level Fusion Method for Mura Defect Recognition
inferredcites →https://doi.org/10.1109/tsmcb.2011.2168604openalex-snapshot
Extreme Learning Machine for Regression and Multiclass Classification
inferredcites →https://doi.org/10.1117/12.301232openalex-snapshot
Automatic blemish detection in liquid crystal flat panel displays
inferredcites →https://doi.org/10.1142/s0219467813500113openalex-snapshot
AUTOMATIC DEFECT CLASSIFICATION OF TFT-LCD PANELS WITH SHAPE, HISTOGRAM AND COLOR FEATURES
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.2307/1403291openalex-snapshot
Generalized Inverses: Theory and Applications
inferredcites →https://doi.org/10.48550/arxiv.1310.1531openalex-snapshot
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition

What points at this work

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