@DeepGravity - Stanford cs231n.part1.rar
1000 MB
Convolutional Neural Networks for Visual Recognition course by Stanford University
⚠️ Bofore downloading: check the course syllabus
Download all lectures and slides here (part1) or watch them on YouTube
Find the other materials (assignments and codes, ...) in the course page
#ConvolutionalNeuralNetworks
#DeepLearning
#Stanford
🔭 @DeepGravity
⚠️ Bofore downloading: check the course syllabus
Download all lectures and slides here (part1) or watch them on YouTube
Find the other materials (assignments and codes, ...) in the course page
#ConvolutionalNeuralNetworks
#DeepLearning
#Stanford
🔭 @DeepGravity
@DeepGravity - Stanford cs231n.part2.rar
1000 MB
Convolutional Neural Networks for Visual Recognition course by Stanford University
⚠️ Bofore downloading: check the course syllabus
Download all lectures and slides here (part2) or watch them on YouTube
Find the other materials (assignments and codes, ...) in the course page
#ConvolutionalNeuralNetworks
#DeepLearning
#Stanford
🔭 @DeepGravity
⚠️ Bofore downloading: check the course syllabus
Download all lectures and slides here (part2) or watch them on YouTube
Find the other materials (assignments and codes, ...) in the course page
#ConvolutionalNeuralNetworks
#DeepLearning
#Stanford
🔭 @DeepGravity
@DeepGravity - Stanford cs231n.part3.rar
229.5 MB
Convolutional Neural Networks for Visual Recognition course by Stanford University
⚠️ Bofore downloading: check the course syllabus
Download all lectures and slides here (part3) or watch them on YouTube
Find the other materials (assignments and codes, ...) in the course page
#ConvolutionalNeuralNetworks
#DeepLearning
#Stanford
🔭 @DeepGravity
⚠️ Bofore downloading: check the course syllabus
Download all lectures and slides here (part3) or watch them on YouTube
Find the other materials (assignments and codes, ...) in the course page
#ConvolutionalNeuralNetworks
#DeepLearning
#Stanford
🔭 @DeepGravity
@DeepGravity - A very cool intro to Keras and CNN.rar
120.8 MB
Download a very cool intro to #Keras and #CNNs
Syllabus:
Keras 1, What is Keras
Keras 2, Installations for #DeepLearning, #Anaconda, #Jupyter Notebook, #Tensorflow, Keras
Keras 3, #NeuralNetwork Regression Model with Keras
Keras 4, Breast Cancer Diagnosis with Neural Networks
Keras 5, Understanding #ConvolutionalNeuralNetworks, Making a Handwritten Digit Calculator
Watch more videos on the related YouTube channel
🔭 @DeepGravity
Syllabus:
Keras 1, What is Keras
Keras 2, Installations for #DeepLearning, #Anaconda, #Jupyter Notebook, #Tensorflow, Keras
Keras 3, #NeuralNetwork Regression Model with Keras
Keras 4, Breast Cancer Diagnosis with Neural Networks
Keras 5, Understanding #ConvolutionalNeuralNetworks, Making a Handwritten Digit Calculator
Watch more videos on the related YouTube channel
🔭 @DeepGravity
How to Visualize Filters and Feature Maps in #ConvolutionalNeuralNetworks
After completing this tutorial, you will know:
* How to develop a visualization for specific filters in a convolutional neural network.
* How to develop a visualization for specific feature maps in a convolutional neural network.
* How to systematically visualize feature maps for each block in a #deep convolutional neural network.
Link
🔭 @DeepGravity
After completing this tutorial, you will know:
* How to develop a visualization for specific filters in a convolutional neural network.
* How to develop a visualization for specific feature maps in a convolutional neural network.
* How to systematically visualize feature maps for each block in a #deep convolutional neural network.
Link
🔭 @DeepGravity
#Classification-driven Single Image Dehazing
Most existing dehazing algorithms often use hand-crafted features or #ConvolutionalNeuralNetworks (#CNN)-based methods to generate clear images using pixel-level Mean Square Error (MSE) loss. The generated images generally have better visual appeal, but not always have better performance for high-level vision tasks, e.g. image classification. In this paper, we investigate a new point of view in addressing this problem. Instead of focusing only on achieving good quantitative performance on pixel-based metrics such as Peak Signal to Noise Ratio (PSNR), we also ensure that the dehazed image itself does not degrade the performance of the high-level vision tasks such as image classification. To this end, we present an unified CNN architecture that includes three parts: a dehazing sub-network (DNet), a classification-driven Conditional #GenerativeAdversarialNetworks sub-network (CCGAN) and a classification sub-network (CNet) related to image classification, which has better performance both on visual appeal and image classification. We conduct comprehensive experiments on two challenging benchmark datasets for fine-grained and object classification: CUB-200-2011 and Caltech-256. Experimental results demonstrate that the proposed method outperforms many recent state-of-the-art single image dehazing methods in terms of image dehazing metrics and classification accuracy.
Link
🔭 @DeepGravity
Most existing dehazing algorithms often use hand-crafted features or #ConvolutionalNeuralNetworks (#CNN)-based methods to generate clear images using pixel-level Mean Square Error (MSE) loss. The generated images generally have better visual appeal, but not always have better performance for high-level vision tasks, e.g. image classification. In this paper, we investigate a new point of view in addressing this problem. Instead of focusing only on achieving good quantitative performance on pixel-based metrics such as Peak Signal to Noise Ratio (PSNR), we also ensure that the dehazed image itself does not degrade the performance of the high-level vision tasks such as image classification. To this end, we present an unified CNN architecture that includes three parts: a dehazing sub-network (DNet), a classification-driven Conditional #GenerativeAdversarialNetworks sub-network (CCGAN) and a classification sub-network (CNet) related to image classification, which has better performance both on visual appeal and image classification. We conduct comprehensive experiments on two challenging benchmark datasets for fine-grained and object classification: CUB-200-2011 and Caltech-256. Experimental results demonstrate that the proposed method outperforms many recent state-of-the-art single image dehazing methods in terms of image dehazing metrics and classification accuracy.
Link
🔭 @DeepGravity
#Transform-Invariant #ConvolutionalNeuralNetworks for Image #Classification and Search
Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of images. Intuitively, with sufficient layers and parameters, hierarchical combinations of convolution (matrix multiplication and non-linear activation) and pooling operations should be able to learn a robust mapping from transformed input images to transform-invariant representations. In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage. This prevents complex dependencies of specific rotation, scale, and translation levels of training images in #CNN models. Rather, each convolutional kernel learns to detect a feature that is generally helpful for producing the transform-invariant answer given the combinatorially large variety of transform levels of its input feature maps. In this way, we do not require any extra training supervision or modification to the optimization process and training images. We show that random transformation provides significant improvements of CNNs on many benchmark tasks, including small-scale image recognition, large-scale image recognition, and image retrieval. The code is available at https://github.com/jasonustc/caffe-multigpu/tree/TICNN.
Paper
🔭 @DeepGravity
Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of images. Intuitively, with sufficient layers and parameters, hierarchical combinations of convolution (matrix multiplication and non-linear activation) and pooling operations should be able to learn a robust mapping from transformed input images to transform-invariant representations. In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage. This prevents complex dependencies of specific rotation, scale, and translation levels of training images in #CNN models. Rather, each convolutional kernel learns to detect a feature that is generally helpful for producing the transform-invariant answer given the combinatorially large variety of transform levels of its input feature maps. In this way, we do not require any extra training supervision or modification to the optimization process and training images. We show that random transformation provides significant improvements of CNNs on many benchmark tasks, including small-scale image recognition, large-scale image recognition, and image retrieval. The code is available at https://github.com/jasonustc/caffe-multigpu/tree/TICNN.
Paper
🔭 @DeepGravity
GitHub
jasonustc/caffe-multigpu
linux && windows compatible caffe. Contribute to jasonustc/caffe-multigpu development by creating an account on GitHub.
P-CapsNets: a General Form of #ConvolutionalNeuralNetworks
We propose Pure CapsNets (P-CapsNets) which is a generation of normal CNNs structurally. Specifically, we make three modifications to current CapsNets. First, we remove routing procedures from CapsNets based on the observation that the coupling coefficients can be learned implicitly. Second, we replace the convolutional layers in CapsNets to improve efficiency. Third, we package the capsules into rank-3 tensors to further improve efficiency. The experiment shows that P-CapsNets achieve better performance than CapsNets with varied routing procedures by using significantly fewer parameters on MNIST&CIFAR10. The high efficiency of P-CapsNets is even comparable to some deep compressing models. For example, we achieve more than 99% percent accuracy on MNIST by using only 3888 parameters. We visualize the capsules as well as the corresponding correlation matrix to show a possible way of initializing CapsNets in the future. We also explore the adversarial robustness of P-CapsNets compared to #CNNs.
Paper
🔭 @DeepGravity
We propose Pure CapsNets (P-CapsNets) which is a generation of normal CNNs structurally. Specifically, we make three modifications to current CapsNets. First, we remove routing procedures from CapsNets based on the observation that the coupling coefficients can be learned implicitly. Second, we replace the convolutional layers in CapsNets to improve efficiency. Third, we package the capsules into rank-3 tensors to further improve efficiency. The experiment shows that P-CapsNets achieve better performance than CapsNets with varied routing procedures by using significantly fewer parameters on MNIST&CIFAR10. The high efficiency of P-CapsNets is even comparable to some deep compressing models. For example, we achieve more than 99% percent accuracy on MNIST by using only 3888 parameters. We visualize the capsules as well as the corresponding correlation matrix to show a possible way of initializing CapsNets in the future. We also explore the adversarial robustness of P-CapsNets compared to #CNNs.
Paper
🔭 @DeepGravity
P-CapsNets: a General Form of #ConvolutionalNeuralNetworks
We propose Pure CapsNets (P-CapsNets) which is a generation of normal #CNNs structurally. Specifically, we make three modifications to current CapsNets. First, we remove routing procedures from CapsNets based on the observation that the coupling coefficients can be learned implicitly. Second, we replace the convolutional layers in CapsNets to improve efficiency. Third, we package the capsules into rank-3 tensors to further improve efficiency. The experiment shows that P-CapsNets achieve better performance than CapsNets with varied routing procedures by using significantly fewer parameters on MNIST&CIFAR10. The high efficiency of P-CapsNets is even comparable to some deep compressing models. For example, we achieve more than 99% percent accuracy on MNIST by using only 3888 parameters. We visualize the capsules as well as the corresponding correlation matrix to show a possible way of initializing CapsNets in the future. We also explore the adversarial robustness of P-CapsNets compared to CNNs.
Paper
🔭 @DeepGravity
We propose Pure CapsNets (P-CapsNets) which is a generation of normal #CNNs structurally. Specifically, we make three modifications to current CapsNets. First, we remove routing procedures from CapsNets based on the observation that the coupling coefficients can be learned implicitly. Second, we replace the convolutional layers in CapsNets to improve efficiency. Third, we package the capsules into rank-3 tensors to further improve efficiency. The experiment shows that P-CapsNets achieve better performance than CapsNets with varied routing procedures by using significantly fewer parameters on MNIST&CIFAR10. The high efficiency of P-CapsNets is even comparable to some deep compressing models. For example, we achieve more than 99% percent accuracy on MNIST by using only 3888 parameters. We visualize the capsules as well as the corresponding correlation matrix to show a possible way of initializing CapsNets in the future. We also explore the adversarial robustness of P-CapsNets compared to CNNs.
Paper
🔭 @DeepGravity