Comparison of Performance by Activation Functions on Deep Image Prior

Author(s):  
Shohei Fujii ◽  
Hitoshi Hayashi
Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 52
Author(s):  
Richard Evan Sutanto ◽  
Sukho Lee

Several recent studies have shown that artificial intelligence (AI) systems can malfunction due to intentionally manipulated data coming through normal channels. Such kinds of manipulated data are called adversarial examples. Adversarial examples can pose a major threat to an AI-led society when an attacker uses them as means to attack an AI system, which is called an adversarial attack. Therefore, major IT companies such as Google are now studying ways to build AI systems which are robust against adversarial attacks by developing effective defense methods. However, one of the reasons why it is difficult to establish an effective defense system is due to the fact that it is difficult to know in advance what kind of adversarial attack method the opponent is using. Therefore, in this paper, we propose a method to detect the adversarial noise without knowledge of the kind of adversarial noise used by the attacker. For this end, we propose a blurring network that is trained only with normal images and also use it as an initial condition of the Deep Image Prior (DIP) network. This is in contrast to other neural network based detection methods, which require the use of many adversarial noisy images for the training of the neural network. Experimental results indicate the validity of the proposed method.


2021 ◽  
Author(s):  
Li Ding ◽  
Yongwei Wang ◽  
Xin Ding ◽  
Kaiwen Yuan ◽  
Ping Wang ◽  
...  
Keyword(s):  

Author(s):  
Fangshu Yang ◽  
Thanh-an Pham ◽  
Nathalie Brandenberg ◽  
Matthias P. Lutolf ◽  
Jianwei Ma ◽  
...  

2020 ◽  
Vol 128 (7) ◽  
pp. 1867-1888 ◽  
Author(s):  
Dmitry Ulyanov ◽  
Andrea Vedaldi ◽  
Victor Lempitsky
Keyword(s):  

2020 ◽  
Author(s):  
Min Jun Park ◽  
Joseph Jennings ◽  
Bob Clapp ◽  
Biondo Biondi

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Di Zhao ◽  
Yanhu Huang ◽  
Feng Zhao ◽  
Binyi Qin ◽  
Jincun Zheng

Deep learning has shown potential in significantly improving performance for undersampled magnetic resonance (MR) image reconstruction. However, one challenge for the application of deep learning to clinical scenarios is the requirement of large, high-quality patient-based datasets for network training. In this paper, we propose a novel deep learning-based method for undersampled MR image reconstruction that does not require pre-training procedure and pre-training datasets. The proposed reference-driven method using wavelet sparsity-constrained deep image prior (RWS-DIP) is based on the DIP framework and thereby reduces the dependence on datasets. Moreover, RWS-DIP explores and introduces structure and sparsity priors into network learning to improve the efficiency of learning. By employing a high-resolution reference image as the network input, RWS-DIP incorporates structural information into network. RWS-DIP also uses the wavelet sparsity to further enrich the implicit regularization of traditional DIP by formulating the training of network parameters as a constrained optimization problem, which is solved using the alternating direction method of multipliers (ADMM) algorithm. Experiments on in vivo MR scans have demonstrated that the RWS-DIP method can reconstruct MR images more accurately and preserve features and textures from undersampled k -space measurements.


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