Shervin Halat

Shervin Halat, Mohammad Rahmati, Ehsan Nazerfard

Recently, significant advancements in artificial intelligence have been attributed to the integration of self-supervised learning (SSL) scheme. While SSL has shown impressive achievements in natural language processing (NLP), its progress in computer vision has comparatively lagged behind. However, the incorporation of contrastive learning into existing visual SSL models has led to considerable progress, often surpassing supervised counterparts. Nonetheless, these improvements have been mostly limited to classification tasks. Moreover, few studies have evaluated visual SSL models in real-world scenarios, as most have focused on datasets with class-wise portrait images, notably ImageNet. Here, we focus on dense prediction tasks using security inspection x-ray images to evaluate our proposed model, Segment Localization (SegLoc). Based upon the Instance Localization (InsLoc) model, SegLoc addresses one of the key challenges of contrastive learning, i.e., false negative pairs of query embeddings. Our pre-training dataset is synthesized by cutting, transforming, and pasting labeled segments from an existing labeled dataset (PIDray) as foregrounds onto instances from an unlabeled dataset (SIXray) as backgrounds. Furthermore, we fully leverage the labeled data by incorporating the concept, one queue per class, into the MoCo-v2 memory bank, thereby avoiding false negative pairs. In our experiments, SegLoc outperformed random initialization by 3% to 6% while underperformed supervised initialization, in terms of AR and AP metrics across different IoU values over 20 to 30 pre-training epochs.

Shervin Halat, Mohammad Mehdi Ebadzadeh, Kiana Amani

Inspired by Double Q-learning algorithm, the Double-DQN (DDQN) algorithm was originally proposed in order to address the overestimation issue in the original DQN algorithm. The DDQN has successfully shown both theoretically and empirically the importance of decoupling in terms of action evaluation and selection in computation of target values; although, all the benefits were acquired with only a simple adaption to DQN algorithm, minimal possible change as it was mentioned by the authors. Nevertheless, there seems a roll-back in the proposed algorithm of DDQN since the parameters of policy network are emerged again in the target value function which were initially withdrawn by DQN with the hope of tackling the serious issue of moving targets and the instability caused by it (i.e., by moving targets) in the process of learning. Therefore, in this paper three modifications to the DDQN algorithm are proposed with the hope of maintaining the performance in the terms of both stability and overestimation. These modifications are focused on the logic of decoupling the best action selection and evaluation in the target value function and the logic of tackling the moving targets issue. Each of these modifications have their own pros and cons compared to the others. The mentioned pros and cons mainly refer to the execution time required for the corresponding algorithm and the stability provided by the corresponding algorithm. Also, in terms of overestimation, none of the modifications seem to underperform compared to the original DDQN if not outperform it. With the intention of evaluating the efficacy of the proposed modifications, multiple empirical experiments along with theoretical experiments were conducted. The results obtained are represented and discussed in this article.