Thompson Sampling for Combinatorial Semi-Bandits

We study the application of the Thompson sampling (TS) methodology to the stochastic combinatorial multi-armed bandit (CMAB) framework. We analyze the standard TS algorithm for the general CMAB, and obtain the first distribution-dependent regret bound of $O(mK_{\max}\log T / \Delta_{\min})$, where $m$ is the number of arms, $K_{\max}$ is the size of the largest super arm, $T$ is the time horizon, and $\Delta_{\min}$ is the minimum gap between the expected reward of the optimal solution and any non-optimal solution. We also show that one cannot directly replace the exact offline oracle with an approximation oracle in TS algorithm for even the classical MAB problem. Then we expand the analysis to two special cases: the linear reward case and the matroid bandit case. When the reward function is linear, the regret of the TS algorithm achieves a better bound $O(m\sqrt{K_{\max}}\log T / \Delta_{\min})$. For matroid bandit, we could remove the independence assumption across arms and achieve a regret upper bound that matches the lower bound for the matroid case. Finally, we use some experiments to show the comparison between regrets of TS and other existing algorithms like CUCB and ESCB

Blind Quality Assessment for in-the-Wild Images via Hierarchical Feature Fusion and Iterative Mixed Database Training

Image quality assessment (IQA) is very important for both end-users and service-providers since a high-quality image can significantly improve the user's quality of experience (QoE) and also benefit lots of computer vision algorithms. Most existing blind image quality assessment (BIQA) models were developed for synthetically distorted images, however, they perform poorly on in-the-wild images, which are widely existed in various practical applications. In this paper, we propose a novel BIQA model for in-the-wild images by addressing two critical problems in this field: how to learn better quality-aware feature representation, and how to solve the problem of insufficient training samples in terms of their content and distortion diversity. Considering that perceptual visual quality is affected by both low-level visual features (e.g. distortions) and high-level semantic information (e.g. content), we first propose a staircase structure to hierarchically integrate the features from intermediate layers into the final feature representation, which enables the model to make full use of visual information from low-level to high-level. Then an iterative mixed database training (IMDT) strategy is proposed to train the BIQA model on multiple databases simultaneously, so the model can benefit from the increase in both training samples and image content and distortion diversity and can learn a more general feature representation. Experimental results show that the proposed model outperforms other state-of-the-art BIQA models on six in-the-wild IQA databases by a large margin. Moreover, the proposed model shows an excellent performance in the cross-database evaluation experiments, which further demonstrates that the learned feature representation is robust to images with diverse distortions and content. The code will be released publicly for reproducible research

DFGC 2022: The Second DeepFake Game Competition

This paper presents the summary report on our DFGC 2022 competition. The DeepFake is rapidly evolving, and realistic face-swaps are becoming more deceptive and difficult to detect. On the contrary, methods for detecting DeepFakes are also improving. There is a two-party game between DeepFake creators and defenders. This competition provides a common platform for benchmarking the game between the current state-of-the-arts in DeepFake creation and detection methods. The main research question to be answered by this competition is the current state of the two adversaries when competed with each other. This is the second edition after the last year's DFGC 2021, with a new, more diverse video dataset, a more realistic game setting, and more reasonable evaluation metrics. With this competition, we aim to stimulate research ideas for building better defenses against the DeepFake threats. We also release our DFGC 2022 dataset contributed by both our participants and ourselves to enrich the DeepFake data resources for the research community (https://github.com/NiCE-X/DFGC-2022).Comment: Accepted by IJCB 202

Unlocking inter-regional flexibility for the HVDC-connected two-area system with a multistage model

In recent years, the deployment of high-voltage direct current (HVDC) tie-lines in power grids has become a prevalent solution in some countries to transmit renewable energy from remote locations to load centers. However, the variability and uncertainty associated with renewable energy generation pose challenges to effectively utilizing this technology. This work proposes a novel multistage planning-operation model, aiming to unlock the potential flexibility in the HVDC transmission system and increase the renewable penetration. By incorporating flexibility, which is essential for accommodating the uncertainty in renewable generation, our model optimally shares the inter-regional flexibility between the sending- and receiving-end grids. One of the key features of our proposed model is its robustness and non-anticipativity, meaning it can account for different levels of uncertainty and make decisions that are suitable for multiple scenarios. This work develops two solution approaches to solve this challenging multistage model with variable uncertainty sets. We validate the proposed approach through a case study conducted on a real-world inter-regional grid. The numerical results demonstrate that our approach effectively unlocks more inter-regional flexibility and assists in increasing the renewable hosting capacity