Learning disentangled representations of data is a fundamental problem in
artificial intelligence. Specifically, disentangled latent representations
allow generative models to control and compose the disentangled factors in the
synthesis process. Current methods, however, require extensive supervision and
training, or instead, noticeably compromise quality. In this paper, we present
a method that learns how to represent data in a disentangled way, with minimal
supervision, manifested solely using available pre-trained networks. Our key
insight is to decouple the processes of disentanglement and synthesis, by
employing a leading pre-trained unconditional image generator, such as
StyleGAN. By learning to map into its latent space, we leverage both its
state-of-the-art quality, and its rich and expressive latent space, without the
burden of training it. We demonstrate our approach on the complex and high
dimensional domain of human heads. We evaluate our method qualitatively and
quantitatively, and exhibit its success with de-identification operations and
with temporal identity coherency in image sequences. Through extensive
experimentation, we show that our method successfully disentangles identity
from other facial attributes, surpassing existing methods, even though they
require more training and supervision.

本文提供了一种使用预训练网络来学习数据的解缠表示的方法，以实现最小的监督，同时展示了该方法在头部图像领域上成功将身份从其他面部属性中解缠并显示出较好的评估结果。

通过潜在空间映射的人脸身份分离

Face Identity Disentanglement via Latent Space Mapping

This paper introduces Task 2 of the DCASE2019 Challenge, titled "Audio
tagging with noisy labels and minimal supervision". This task was hosted on the
Kaggle platform as "Freesound Audio Tagging 2019". The task evaluates systems
for multi-label audio tagging using a large set of noisy-labeled data, and a
much smaller set of manually-labeled data, under a large vocabulary setting of
80 everyday sound classes. In addition, the proposed dataset poses an acoustic
mismatch problem between the noisy train set and the test set due to the fact
that they come from different web audio sources. This can correspond to a
realistic scenario given by the difficulty in gathering large amounts of
manually labeled data. We present the task setup, the FSDKaggle2019 dataset
prepared for this scientific evaluation, and a baseline system consisting of a
convolutional neural network. All these resources are freely available.

介绍了 DCASE2019 挑战赛的任务 2，旨在使用大量嘈杂标注数据和少量手动标注数据下，评估具有 80 个声音类别的大词汇设置中的多标签音频标记系统，同时提出了一种基于卷积神经网络的基线系统和 FSDKaggle2019 数据集。