图像分类,顾名思义,是一个输入图像,输出对该图像内容分类的描述的问题。它是计算机视觉的核心,实际应用广泛。

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准确的图像和视频分类对于广泛的计算机视觉应用非常重要,从识别有害内容,到使视障人士更容易地获得产品,再到帮助人们更容易地在市场等产品上买卖东西。Facebook AI正在开发替代方法来训练我们的人工智能系统,这样我们就可以用更少的标记训练数据来做更多的事情,而且即使在无法获得大量高质量的标记数据集的情况下,也能提供准确的结果。今天,我们分享一个多功能的新模型训练技术的细节,为图像和视频分类系统提供最先进的准确性。

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Current state-of-the-art methods of image classification using convolutional neural networks are often constrained by both latency and power consumption. This places a limit on the devices, particularly low-power edge devices, that can employ these methods. Spiking neural networks (SNNs) are considered to be the third generation of artificial neural networks which aim to address these latency and power constraints by taking inspiration from biological neuronal communication processes. Before data such as images can be input into an SNN, however, they must be first encoded into spike trains. Herein, we propose a method for encoding static images into temporal spike trains using edge detection and an adaptive signal sampling method for use in SNNs. The edge detection process consists of first performing Canny edge detection on the 2D static images and then converting the edge detected images into two X and Y signals using an image-to-signal conversion method. The adaptive signaling approach consists of sampling the signals such that the signals maintain enough detail and are sensitive to abrupt changes in the signal. Temporal encoding mechanisms such as threshold-based representation (TBR) and step-forward (SF) are then able to be used to convert the sampled signals into spike trains. We use various error and indicator metrics to optimize and evaluate the efficiency and precision of the proposed image encoding approach. Comparison results between the original and reconstructed signals from spike trains generated using edge-detection and adaptive temporal encoding mechanism exhibit 18x and 7x reduction in average root mean square error (RMSE) compared to the conventional SF and TBR encoding, respectively, while used for encoding MNIST dataset.

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