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Proceedings ArticleDOI
Duc Minh Vo, Thai Hoang Le 
01 Sep 2016
17 Citations
Our study puts the deep generic features from CNN and SVM together which is more efficient than CNN only.
Compared to transfer learning-based classification, the adopted strategy of CNN-SVM has lesser computations and memory requirements.
The experimental results indicates that compared with traditional SVM+HOG method and the baseline Faster R-CNN, the improved model can achieve better performance.
The experimental results prove that the proposed technique has outperformed SVM and CNN in terms of accuracy, PSNR, MSE and other performance parameters.
According to the experimental results, the proposed model outperforms SVM, LSTM, and CNN on the two datasets.

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How is cnn used in computer vision?5 answersConvolutional neural networks (CNNs) are widely used in computer vision for various tasks. They have been successful in modeling both neural activity and behavior in visual tasks, providing insights into biological vision. CNNs have been applied to style transfer applications, where they change the style of a content image into that of a style image. To deal with high-resolution images, network compression and layer-chaining techniques have been proposed to reduce computational complexity and feature map size. Deep CNNs have shown exemplary performance in image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. Architectural innovations, such as spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention, have significantly improved the representational capacity of deep CNNs. CNN models can be used to predict errors and response times in object recognition tasks, and combining CNN object representations with cognitive models can account for performance at the individual-participant and item levels. While CNNs have advanced reconstruction tasks in computer vision, convolutional sparse coding (CSC) approaches can be more effective for certain types of inverse problems, such as JPEG artifact reduction and non-rigid trajectory reconstruction.
What are the advantages and disadvantages of SVM?3 answersSupport Vector Machines (SVMs) have several advantages in drug discovery and pattern recognition. SVMs are powerful approaches in early drug discovery, allowing for the classification of active or inactive compounds and prioritization of substances for virtual screening. They are also effective in solving small sample, non-linear, and high-dimensional pattern recognition problems. Additionally, SVMs can handle nonlinear problems and offer high accuracy for recognition and regression tasks. However, there are some limitations to SVMs. One limitation is that SVMs only learn from labeled data, which can be costly and time-consuming to obtain, but semi-supervised learning methods can overcome this limitation by incorporating information from unlabeled data. Another limitation is that SVMs assume data is drawn from an independent and identically distributed underlying distribution, which may not hold true in real-life problems.
Training of cnn?5 answersTraining of convolutional neural networks (CNNs) is a memory-intensive process that requires deeper networks and more training data for better performance. The training procedure consists of a forward pass (FP) and a backward pass (BP), with BP accounting for a significant portion of the memory accesses. To address the memory bottlenecks and conserve computational resources, near-data processing can be employed during CNN training. This approach reduces the amount of data transmission between the host and the storage, and brings the convolution, pooling, and training work to powerful computational storage devices (CSDs). Additionally, increasing the amount of labeled training data is crucial for improving the classification accuracy of automated diagnosis systems. One method to achieve this is by extracting patches from endoscopic videos that show the same content as the images in the database, but under different viewing conditions.
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