Talk 1

Title: Temporal Antialiasing Techniques (TAA)

Speaker: Niting Wang

Abstract:

Temporal Antialiasing (TAA), formally defined as temporally-amortized supersampling, is the most widely used antialiasing technique in today’s real-time renderers and game engines. This survey provides a systematic overview of this technique. We first review the history of TAA, its development path and related work. We then identify the two main sub-components of TAA, sample accumulation and history validation, and discuss algorithmic and implementation options. As temporal upsampling is becoming increasingly relevant to today’s game engines, we propose an extension of our TAA formulation to cover a variety of temporal upsampling techniques. Despite the popularity of TAA, there are still significant unresolved technical challenges that affect image quality in many scenarios. We provide an in-depth analysis of these challenges, and review existing techniques for improvements. Finally, we summarize popular algorithms and topics that are closely related to TAA. We believe the rapid advances in those areas may either benefit from or feedback into TAA research and development.

Supervisor: Xiao Liang, Jingwen Wang

Talk 2

Title:Functional Mechanism: Regression Analysis under Differential Privacy

Speaker: Jianxin Pei

Abstract:

ϵ-differential privacy is the state-of-the-art model for releasing sensitive information while protecting privacy. Numerous methods have been proposed to enforce ϵ-differential privacy in various analytical tasks, e.g., regression analysis. Existing solutions for regression analysis, however, are either limited to non-standard types of regression or unable to produce accurate regression results. Motivated by this, we propose the Functional Mechanism, a differentially private method designed for a large class of optimization-based analyses. The main idea is to enforce ϵ-differential privacy by perturbing the objective function of the optimization problem,rather than its results. As case studies, we apply the functional mechanism to address two most widely used regression models,namely, linear regression and logistic regression. Both theoretical analysis and thorough experimental evaluations show that the functional mechanism is highly effective and efficient, and it significantly outperforms existing solutions.

Supervisor: Ying Liu, Kexian Zheng

 

Time:16:00  September 24, 2020

Address:MingLi Buliding C1102

Chair: Yang Yang