**Anycost GAN** is a type of generative adversarial network for image synthesis and editing. Given an input image, we project it into the latent space with encoder $E$ and backward optimization. We can modify the latent code with user input to edit the image. During editing, a sub-generator of small cost is used for fast and interactive preview; during idle time, the full cost generator renders the final, high-quality output. The outputs from the full and sub-generators are visually consistent during projection and editing.

A generic way of representing and interpolating labels, which allows straightforward extension of any kind of [mixup](https://paperswithcode.com/method/mixup) to deep metric learning for a large class of loss functions.

Metrix

It Takes Two to Tango: Mixup for Deep Metric Learning

Anycost GAN

Anycost GANs for Interactive Image Synthesis and Editing

**DeepMask** is an object proposal algorithm based on a convolutional neural network. Given an input image patch, DeepMask generates a class-agnostic mask and an associated score which estimates the likelihood of the patch fully containing a centered object (without any notion of an object category). The core of the model is a ConvNet which jointly predicts the mask and the object score. A large part of the network is shared between those two tasks: only the last few network
layers are specialized for separately outputting a mask and score prediction.

Source	Anycost GANs for Interactive Image Synthesis and Editing
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com