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In this paper, the authors introduce Fashion-MNIST, a new dataset designed to provide a more challenging test for machine learning algorithms than the traditional MNIST dataset. The Fashion-MNIST dataset consists of 70,000 images of various categories such as shirts, dresses, coats, hats, scarves, bags, and shoes. Each class has a different texture, color, and style, making it more diverse than MNIST. The authors also introduce several techniques to improve the performance of machine learning models on Fashion-MNIST, including multi-column deep neural networks, emnist (an extension of MNIST to handwritten letters), imagenet (a large-scale hierarchical image database), and regularization techniques using dropconnect. The authors evaluate several machine learning algorithms on Fashion-MNIST, showing that CNNs perform significantly better than SVMs, and that the use of dropconnect regularization improves the performance of both types of models. Overall, this paper provides a valuable resource for researchers working on image classification tasks and demonstrates the potential of using Fashion-MNIST as a more challenging and diverse alternative to MNIST.