4 accepted papers
CT-to-PET synthesis aims to synthesize PET images from the widely available and lower-cost CT scans to address the high cost and additional radiation exposure associated with PET scanning. However, CT-to-PET synthesis faces two key challenges due to the sequential correlation of volumetric imaging:
Self-supervised stereo matching has drawn attention due to its ability to estimate disparity without needing ground-truth data. However, existing self-supervised stereo matching methods heavily rely on the photo-metric consistency assumption, which is vulnerable to natural disturbances, resulting in…
It is known that deep neural networks are vulnerable to adversarial attacks. Although Automatic Speaker Verification (ASV) built on top of deep neural networks exhibits robust performance in controlled scenarios, many studies confirm that ASV is vulnerable to adversarial attacks. The lack of a stand…
Market making (MM) via Reinforcement Learning (RL) has attracted significant attention in financial trading. Most existing RL-based MM methods focus on optimizing single-price level strategies which fail at frequent order cancellations and loss of queue priority. By comparison, strategies involving…