Training deep neural networks results in strong learned representations that show good generalization capabilities. In most cases, training involves iterative modification of all weights in the network via back-propagation. In Extreme Learning Machines, it has been suggested to set the first layer of a network to fixed random values instead of learning it. In this paper, we propose to take this approach a step further and fix almost all layers of a deep convolutional neural network, allowing only a small portion of the weights to be learned. As our experiments show, fixing even the majority of the parameters of the network often results in performance which is on par with the performance of learning all of them. The implications of this intriguing property of deep neural networks are discussed and we suggest practical ways to harness it to create more robust and compact representations.
A very recent result is that of @cite_6 , showing - quite surprisingly - that using a fixed, Hadamard matrix @cite_9 for a final classification layer does not hinder the performance of a classifier. In contrast, we do not impose any constraints on the values of any of the fixed weights (except drawing them from the same distribution as that of the learned ones), and evaluate the effect of fixing many different subsets of weights throughout the network.
@cite_9 @cite_6
401986301 2963940260
Neural networks are commonly used as models for classification for a wide variety of tasks. Typically, a learned affine transformation is placed at the end of such models, yielding a per-class value used for classification. This classifier can have a vast number of parameters, which grows linearly with the number of possible classes, thus requiring increasingly more resources. In this work we argue that this classifier can be fixed, up to a global scale constant, with little or no loss of accuracy for most tasks, allowing memory and computational benefits. Moreover, we show that by initializing the classifier with a Hadamard matrix we can speed up inference as well. We discuss the implications for current understanding of neural network models. Performing the inference step of deep learning in resource constrained environments, such as embedded devices, is challenging. Success requires optimization at both software and hardware levels. Low precision arithmetic and specifically low precision fixed-point number systems have become the standard for performing deep learning inference. However, representing non-uniform data and distributed parameters (e.g. weights) by using uniformly distributed fixed-point values is still a major drawback when using this number system. Recently, the posit number system was proposed, which represents numbers in a non-uniform manner. Therefore, in this paper we are motivated to explore using the posit number system to represent the weights of Deep Convolutional Neural Networks. However, we do not apply any quantization techniques and hence the network weights do not require re-training. The results of this exploration show that using the posit number system outperformed the fixed point number system in terms of accuracy and memory utilization.