In this work, we propose a novel architecture (and several variants thereof)
based on quantum cryptographic primitives with provable privacy and security
guarantees regarding membership inference attacks on generative models. Our
architecture can be used on top of any existing classical or quantum generative
models. We argue that the use of quantum gates associated with unitary
operators provides inherent advantages compared to standard Differential
Privacy based techniques for establishing guaranteed security from all
polynomial-time adversaries.

本研究提出了一种基于量子密码原理的全新架构，可用于对生成模型的成员推理攻击进行保障隐私和安全性，通过量子门和酉算子相结合相较于标准差分隐私技术，在保护安全性方面具有内在优势。

隐私与效用权衡的量子解决方案

Quantum Solutions to the Privacy vs. Utility Tradeoff

We study the problem of compositional zero-shot learning for object-attribute
recognition. Prior works use visual features extracted with a backbone network,
pre-trained for object classification and thus do not capture the subtly
distinct features associated with attributes. To overcome this challenge, these
studies employ supervision from the linguistic space, and use pre-trained word
embeddings to better separate and compose attribute-object pairs for
recognition. Analogous to linguistic embedding space, which already has unique
and agnostic embeddings for object and attribute, we shift the focus back to
the visual space and propose a novel architecture that can disentangle
attribute and object features in the visual space. We use visual decomposed
features to hallucinate embeddings that are representative for the seen and
novel compositions to better regularize the learning of our model. Extensive
experiments show that our method outperforms existing work with significant
margin on three datasets: MIT-States, UT-Zappos, and a new benchmark created
based on VAW. The code, models, and dataset splits are publicly available at
this https URL

本研究针对目标属性识别的组合式零样本学习问题，提出了一种新颖的体系结构，用于在视觉空间中区分属性和对象特征并使用视觉分解特征产生代表性的嵌入，旨在更好地正则化模型的学习，实验结果表明我们的方法在 MIT-States，UT-Zappos 和基于 VAW 的新基准上的性能显著优于现有工作。