Convolutional Neural Network – Support Vector Machine (CNN-SVM) google
Convolutional neural networks (CNNs) are similar to ‘ordinary’ neural networks in the sense that they are made up of hidden layers consisting of neurons with ‘learnable’ parameters. These neurons receive inputs, performs a dot product, and then follows it with a non-linearity. The whole network expresses the mapping between raw image pixels and their class scores. Conventionally, the Softmax function is the classifier used at the last layer of this network. However, there have been studies (Alalshekmubarak and Smith, 2013; Agarap, 2017; Tang, 2013) conducted to challenge this norm. The cited studies introduce the usage of linear support vector machine (SVM) in an artificial neural network architecture. This project is yet another take on the subject, and is inspired by (Tang, 2013). Empirical data has shown that the CNN-SVM model was able to achieve a test accuracy of ~99.04% using the MNIST dataset (LeCun, Cortes, and Burges, 2010). On the other hand, the CNN-Softmax was able to achieve a test accuracy of ~99.23% using the same dataset. Both models were also tested on the recently-published Fashion-MNIST dataset (Xiao, Rasul, and Vollgraf, 2017), which is suppose to be a more difficult image classification dataset than MNIST (Zalandoresearch, 2017). This proved to be the case as CNN-SVM reached a test accuracy of ~90.72%, while the CNN-Softmax reached a test accuracy of ~91.86%. The said results may be improved if data preprocessing techniques were employed on the datasets, and if the base CNN model was a relatively more sophisticated than the one used in this study. …

Dynamically Routed Network (SkipNet) google
Increasing depth and complexity in convolutional neural networks has enabled significant progress in visual perception tasks. However, incremental improvements in accuracy are often accompanied by exponentially deeper models that push the computational limits of modern hardware. These incremental improvements in accuracy imply that only a small fraction of the inputs require the additional model complexity. As a consequence, for any given image it is possible to bypass multiple stages of computation to reduce the cost of forward inference without affecting accuracy. We exploit this simple observation by learning to dynamically route computation through a convolutional network. We introduce dynamically routed networks (SkipNets) by adding gating layers that route images through existing convolutional networks and formulate the routing problem in the context of sequential decision making. We propose a hybrid learning algorithm which combines supervised learning and reinforcement learning to address the challenges of inherently non-differentiable routing decisions. We show SkipNet reduces computation by 30 – 90% while preserving the accuracy of the original model on four benchmark datasets. We compare SkipNet with SACT and ACT to show SkipNet achieves better accuracy with lower computation. …

Out-of-Distribution Detector for Neural Networks (ODIN) google
We consider the problem of detecting out-of-distribution examples in neural networks. We propose ODIN, a simple and effective out-of-distribution detector for neural networks, that does not require any change to a pre-trained model. Our method is based on the observation that using temperature scaling and adding small perturbations to the input can separate the softmax score distributions of in- and out-of-distribution samples, allowing for more effective detection. We show in a series of experiments that our approach is compatible with diverse network architectures and datasets. It consistently outperforms the baseline approach[1] by a large margin, establishing a new state-of-the-art performance on this task. For example, ODIN reduces the false positive rate from the baseline 34.7% to 4.3% on the DenseNet (applied to CIFAR-10) when the true positive rate is 95%. We theoretically analyze the method and prove that performance improvement is guaranteed under mild conditions on the image distributions. …

Advertisements