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.

**EMQAP**, or **E-Manual Question Answering Pipeline**, is an approach for answering questions pertaining to electronics devices. Built upon the pretrained [RoBERTa](https://paperswithcode.com/method/roberta), it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section.

EMQAP

Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework

Metrix

It Takes Two to Tango: Mixup for Deep Metric Learning

**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.

Source	It Takes Two to Tango: Mixup for Deep Metric Learning
Year	2000
Data Source	Papers with Code archive (CC BY-SA)