A Novel Method Based on Deep Convolutional Neural Networks for Wafer Semiconductor Surface Defect Inspection

2020 ◽  
Vol 69 (12) ◽  
pp. 9668-9680 ◽  
Author(s):  
Guojun Wen ◽  
Zhijun Gao ◽  
Qi Cai ◽  
Yudan Wang ◽  
Shuang Mei
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Surface Defect ◽  
Semiconductor Surface ◽  
Defect Inspection ◽  
Deep Convolutional Neural Networks ◽  
Novel Method

scholarly journals Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks

2020 ◽  
Author(s):  
Tuan Hoang ◽  
Thanh-Toan Do ◽  
Tam V. Nguyen ◽  
Ngai-Man Cheung
Keyword(s):  
Neural Networks ◽  
Cost Function ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Gradient Descent ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Deep Convolutional Neural Networks ◽  
Novel Method ◽  
The Cost

This paper proposes two novel techniques to train deep convolutional neural networks with low bit-width weights and activations. First, to obtain low bit-width weights, most existing methods obtain the quantized weights by performing quantization on the full-precision network weights. However, this approach would result in some mismatch: the gradient descent updates full-precision weights, but it does not update the quantized weights. To address this issue, we propose a novel method that enables direct updating of quantized weights with learnable quantization levels to minimize the cost function using gradient descent. Second, to obtain low bit-width activations, existing works consider all channels equally. However, the activation quantizers could be biased toward a few channels with high-variance. To address this issue, we propose a method to take into account the quantization errors of individual channels. With this approach, we can learn activation quantizers that minimize the quantization errors in the majority of channels. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on the image classification task, using AlexNet, ResNet and MobileNetV2 architectures on CIFAR-100 and ImageNet datasets.

scholarly journals Learning Class-Specific Features with Class Regularization for Videos

2020 ◽  
Vol 10 (18) ◽  
pp. 6241
Author(s):  
Alexandros Stergiou ◽  
Ronald Poppe ◽  
Remco C. Veltkamp
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
General Feature ◽  
Deep Convolutional Neural Networks ◽  
Systematic Improvement ◽  
Novel Method ◽  
Different Depths ◽  
Explicit Correlation ◽  
Fully Connected ◽  
The Given

One of the main principles of Deep Convolutional Neural Networks (CNNs) is the extraction of useful features through a hierarchy of kernels operations. The kernels are not explicitly tailored to address specific target classes but are rather optimized as general feature extractors. Distinction between classes is typically left until the very last fully-connected layers. Consequently, variances between classes that are relatively similar are treated the same way as variations between classes that exhibit great dissimilarities. In order to directly address this problem, we introduce Class Regularization, a novel method that can regularize feature map activations based on the classes of the examples used. Essentially, we amplify or suppress activations based on an educated guess of the given class. We can apply this step to each minibatch of activation maps, at different depths in the network. We demonstrate that this improves feature search during training, leading to systematic improvement gains on the Kinetics, UCF-101, and HMDB-51 datasets. Moreover, Class Regularization establishes an explicit correlation between features and class, which makes it a perfect tool to visualize class-specific features at various network depths.

scholarly journals Comparing part-based representations of deep convolutional neural networks with those of human vision through representational similarity analysis

2021 ◽  
Author(s):  
Jiaqi Huang ◽  
Peter Gerhardstein
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Human Vision ◽  
Behavioral Experiment ◽  
Similarity Analysis ◽  
Deep Convolutional Neural Networks ◽  
Representational Similarity Analysis ◽  
Novel Method ◽  
Semantic Parts

Multiple theories of human object recognition argue for the importance of semantic parts in the formation of intermediate representations. However, the role of semantic parts in Deep Convolutional Neural Networks (DCNN), which encapsulate the most recent and successful computer vision models, is poorly examined. We extract representations of DCNNs corresponding to differential performance with stimuli in which different parts of the same exemplar are deleted, and then compare these representations with those of human observers obtained in a behavioral experiment, using representational similarity analysis (RSA). We find that DCNN representations correlate strongly with those of observers, while acknowledging that these DCNN representations may not be part-based given an equally high correlation between DCNN output and part size. Additionally, the exemplars incorrectly identified by DCNNs tend to have less “human-like” representations, which demonstrates RSA as a potential novel method for interpreting error in intermediate processes of recognition of DCNNs.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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