SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization

Advanced data augmentation strategies have widely been studied to improve the generalization ability of deep learning models. Regional dropout is one of the popular solutions that guides the model to focus on less discriminative parts by randomly removing image regions, resulting in improved regularization. However, such information removal is undesirable. On the other hand, recent strategies suggest to randomly cut and mix patches and their labels among training images, to enjoy the advantages of regional dropout without having any pointless pixel in the augmented images. We argue that the random selection of the patch may not necessarily represent any information about the corresponding object and thereby mixing the labels according to that uninformative patch enables the model to learn unexpected feature representation. Therefore, we propose SaliencyMix that carefully selects a representative image patch with the help of a saliency map and mixes this indicative patch with the target image that leads the model to learn more appropriate feature representation. SaliencyMix achieves a new state-of-the-art top-1 error of 20.09% on ImageNet classification using ResNet-101 architecture and also improves the model robustness against adversarial perturbations. Furthermore, SaliencyMix trained model helps to improve the object detection performance.

Center Emphasized Visual Saliency and Contrast-based Full Reference Image Quality Index

Objective Image Quality Assessment (IQA) is imperative in this multimedia-intensive world to asses the visual quality of an image close to the human ability. There are many parameters that bring human attention to an image and if the center part contains any visually salient information then it draws the attention even more. To the best of our knowledge, any previous IQA method did not give extra importance to the center part. In this paper, we propose a full reference image quality assessment (FR-IQA) approach using visual saliency and contrast, however, we give extra attention to the center by raising-up sensitivity of the similarity maps in that region. We evaluated our method on three popular benchmark databases (TID2008, CSIQ and LIVE) and compared with 13 state-of-the-art approaches which reveal the stronger correlation of our method with human evaluated values. The prediction of quality score is consistent for distortion-specific as well as distortion-independent cases. Moreover, faster processing makes it applicable to any real-time application. The MATLAB pcode is publicly available online to test the algorithm and can be found at http://layek.khu.ac.kr/CEQI.

Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes

In recent years, reinforcement learning has achieved many remarkable successes due to the growing adoption of deep learning techniques and the rapid growth in computing power. Nevertheless, it is well-known that flat reinforcement learning algorithms are often not able to learn well and data-efficient in tasks having hierarchical structures, e.g. consisting of multiple subtasks. Hierarchical reinforcement learning is a principled approach that is able to tackle these challenging tasks. On the other hand, many real-world tasks usually have only partial observability in which state measurements are often imperfect and partially observable. The problems of RL in such settings can be formulated as a partially observable Markov decision process (POMDP). In this paper, we study hierarchical RL in POMDP in which the tasks have only partial observability and possess hierarchical properties. We propose a hierarchical deep reinforcement learning approach for learning in hierarchical POMDP. The deep hierarchical RL algorithm is proposed to apply to both MDP and POMDP learning. We evaluate the proposed algorithm on various challenging hierarchical POMDP.