Network pruning is a commonly used strategy to reduce the memory and storage footprints of CNNs on mobile devices. In this article, we propose customized versions of the sparse matrix multiplication algorithm to speed up inference on mobile devices and make it more energy efficient. Specifically, ...
Meanwhile, at the stage of character recognition, data annotation is heavy and time-consuming, giving rise to a large burden on training a better model. We devise an algorithm to generate annotated training data automatically and approximate the data from the real scenes. Our system used for ...
By looking at the maximimum activation of particular neurons we can visualize what patters are larned in particular filters. The Algorithm We start with a pretrained Vgg16 model and a noisy image as seen below. This image is passed through the network. At a particular layer the gradient ...
c library deep-learning neural-network opencl transformer lstm convolutional-layers deeplearning residual-layers residual-networks backpropagation optimization-algorithms softmax adam-optimizer avarage nesterov adam-algorithm dropout-layers Updated Sep 29, 2023 C rachelsohzc / Simple-stock-prediction-with...
3.1. Fast R-CNN Advantages The Fast R-CNN algorithm outperforms R-CNN because the feature extraction takes place once per image, in order for the RoI projections to be generated, instead of performing a convolution forward pass for each object proposal per image, in the case of R-CNN. 4...
在CIFAR上,一个简单的纯MLP模型显示了非常接近CNN的性能。通过在传统CNN中插入RepMLP,我们将ResNets在ImageNet上的准确率提高了1.8%,在人脸识别上提高了2.9%,在Cityscapes上提高了2.3%,且有着更低的FLOPs。我们有趣的发现强调,将FC的全局表征能力和位置感知与卷积的局部先验相结合,可以以更快的速度提高神经网络在...
Our layer search algorithm leads to the discovery of EvoNorms, a set of new normalization-activation layers that go beyond existing design patterns. Several of these layers enjoy the property of being independent from the batch statistics. Our experiments show that EvoNorms: excel on a variety...
The benefit of using these functions is that the algorithm selected is the best choice. However, since actual performance tests are being run, these functions can be time and resource-intensive. After an algorithm is chosen, our heuristics specify additional low-level details; for example, tile...
The Stochastic Multi Gradient Descent Algorithm implementation in Python3 is for usage with Keras and adopted from paper of S. Liu and L. N. Vicente: "The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning". It is combined with ...
Neural network model capacity is controlled both by the number of nodes and the number of layers in the model. A model with a single hidden layer and sufficient number of nodes has the capability of learning any mapping function, but the chosen learning algorithm may or may not be able to...