Teacher:In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Teacher: Now, understand the problem? Solve this instance: Deep neural networks have dramatically advanced the state of the art for many<lb>areas of machine learning. Recently they have been shown to have a remarkable<lb>ability to generate highly complex visual artifacts such as images and text rather<lb>than simply recognize them.<lb>In this work we use neural networks to effectively invert low-dimensional face<lb>embeddings while producing realistically looking consistent images. Our contri-<lb>bution is twofold, first we show that a gradient ascent style approaches can be<lb>used to reproduce consistent images, with a help of a guiding image. Second, we<lb>demonstrate that we can train a separate neural network to effectively solve the<lb>minimization problem in one pass, and generate images in real-time. We then<lb>evaluate the loss imposed by using a neural network instead of the gradient descent<lb>by comparing the final values of the minimized loss function.
Student:
Inverting face embeddings with convolutional neural networks