Going Deeper with Convolutions

This paper introduces a new deep learning architecture called Inception, which aims to improve both the speed and accuracy of image recognition. The Inception architecture allows for increased network depth and width while keeping the computational cost manageable. It achieves this by using multiple convolutional filter sizes in parallel and employing dimensionality reduction techniques. The authors demonstrate that this architecture achieves state-of-the-art results on the ImageNet classification challenge.