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نتیجه جستجو - Adversarial training

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 AI-GAN: Asynchronous interactive generative adversarial network for single image rain removal
هوش مصنوعی -GAN: شبکه مواد تخاصمی ناهمزمان برای حذف باران با یک تصویر-2020
Single image rain removal plays an important role in numerous multimedia applications. Existing algo- rithms usually tackle the deraining problem by the way of signal removal, which lead to over-smoothness and generate unexpected artifacts in de-rained images. This paper addresses the deraining problem from a completely different perspective of feature-wise disentanglement, and introduces the interactions and constraints between two disentangled latent spaces. Specifically, we propose an Asynchronous Interactive Generative Adversarial Network (AI-GAN) to progressively disentangle the rainy image into background and rain spaces in feature level through a two-branch structure. Each branch employs a two-stage synthe- sis strategy and interacts asynchronously by exchanging feed-forward information and sharing feedback gradients, achieving complementary adversarial optimization. This ‘adversarial’ is not only the ‘adversar- ial’ between the generator and the discriminator, but also means that the two generators are entangled, and interact with each other in the optimization process. Extensive experimental results demonstrate that AI-GAN outperforms state-of-the-art deraining methods and benefits various typical multimedia applica- tions such as Image/Video Coding, Action Recognition, and Person Re-identification.
Keywords: Feature-wise disentanglement | Asynchronous and interactive | Single image deraining | Complementary adversarial training
مقاله انگلیسی
2 A novel method for malware detection on ML-based visualization technique
یک روش جدید برای شناسایی بدافزارها در تکنیک تجسم مبتنی بر ML-2020
Malware detection is one of the challenging tasks in network security. With the flourishment of network techniques and mobile devices, the threat from malwares has been of an increasing significance, such as metamorphic malwares, zero-day attack, and code obfuscation, etc . Many machine learning (ML)-based malware detection methods are proposed to address this problem. However, considering the attacks from adversarial examples (AEs) and exponential increase in the malware variant thriving nowadays, malware detection is still an active field of research. To overcome the current limitation, we proposed a novel method using data visualization and adversarial training on ML-based detectors to efficiently detect the different types of malwares and their variants. Experimental results on the MS BIG malware database and the Ember database demonstrate that the proposed method is able to prevent the zero-day attack and achieve up to 97.73% accuracy, along with 96.25% in average for all the malwares tested.
Keywords: Malware detection | Adversarial training | Adversarial examples | Image texture | Data visualization
مقاله انگلیسی
3 APL: Adversarial Pairwise Learning for Recommender Systems
APL: یادگیری خصمانه طرف مقابل برای سیستم های توصیه گر-2019
The main objective of recommender systems is to help users select their desired items, where a ma- jor challenge is modeling users’ preferences based on their historical feedback (e.g., clicks, purchases or check-ins). Recently, several recommendation models have utilized the adversarial technique, which has been successfully used to capture real data distributions in various domains (e.g., computer vision). Nevertheless, the training process of the original adversarial technique is very slow and unstable in the domain of recommender systems. First, the sparsity of the implicit feedback dataset aggravates the inherently intractable adversarial training process. Second, since the original adversarial model is de- signed for differentiable values (e.g., images), the discrete items also increase the training difficulty. To cope with these issues, we propose a novel method named Adversarial Pairwise Learning (APL), which unifies generative and discriminative models via adversarial learning. Specifically, based on the weaker assumption that the user prefers observed items over generated items, APL exploits pairwise ranking to accelerate the convergence and enhance the stability of adversarial learning. Additionally, a differ- entiable procedure is adopted to replace the discrete item sampling to optimize APL via backpropaga- tion and stabilize the training process. Extensive experiments under multiple recommendation scenarios demonstrate APL’s effectiveness, fast convergence and stability. Our implementation of APL is available at: https://github.com/ZhongchuanSun/APL.
Keywords: Adversarial learning | Pairwise ranking | Matrix factorization | Recommender systems
مقاله انگلیسی
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