Adversarial

How to Implement GAN Hacks to Train Stable Generative Adversarial Networks

Generative Adversarial Networks, or GANs, are challenging to train. This is because the architecture involves both a generator and a discriminator model that compete in a zero-sum game. It means...

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Tips for Training Stable Generative Adversarial Networks

The Empirical Heuristics, Tips, and Tricks That You Need to Know to Train Stable Generative Adversarial Networks (GANs). Generative Adversarial Networks, or GANs for short, are an approach to generative...

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A Gentle Introduction to Generative Adversarial Networks (GANs)

Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods, such as convolutional neural networks. Generative modeling is an unsupervised learning task in...

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18 Impressive Applications of Generative Adversarial Networks (GANs)

A Generative Adversarial Network, or GAN, is a type of neural network architecture for generative modeling. Generative modeling involves using a model to generate new examples that plausibly come from...

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Best Resources for Getting Started With Generative Adversarial Networks (GANs)

Generative Adversarial Networks, or GANs, are a type of deep learning technique for generative modeling. GANs are the techniques behind the startlingly photorealistic generation of human faces, as well as...

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Analysis and Improvement of Adversarial Training in DQN Agents With Adversarially-Guided Exploration (AGE). (arXiv:1906.01119v1 [cs.LG])

This paper investigates the effectiveness of adversarial training in enhancing the robustness of Deep Q-Network (DQN) policies to state-space perturbations. We first present a formal analysis of adversarial training in...

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RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies. (arXiv:1906.01110v1 [cs.LG])

This paper investigates the resilience and robustness of Deep Reinforcement Learning (DRL) policies to adversarial perturbations in the state space. We first present an approach for the disentanglement of vulnerabilities...

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Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach. (arXiv:1905.06484v1 [cs.CV])

[ad_1] Recently, semi-supervised learning methods based on generative adversarial networks (GANs) have received much attention. Among them, two distinct approaches have achieved competitive results on a variety of benchmark datasets....

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Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning. (arXiv:1904.02390v1 [cs.RO])

[ad_1] In order to enable high-quality decision making and motion planning of intelligent systems such as robotics and autonomous vehicles, accurate probabilistic predictions for surrounding interactive objects is a crucial...

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Generative Adversarial Networks for text using word2vec intermediaries. (arXiv:1904.02293v1 [cs.CL])

[ad_1] Generative adversarial networks (GANs) have shown considerable success, especially in the realistic generation of images. In this work, we apply similar techniques for the generation of text. We propose...

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Bridging Adversarial Robustness and Gradient Interpretability. (arXiv:1903.11626v1 [cs.LG])

[ad_1] Adversarial training is a training scheme designed to counter adversarial attacks by augmenting the training dataset with adversarial examples. Surprisingly, several studies have observed that loss gradients from adversarially...

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Humans can predict how machines (mis)classify adversarial images – VentureBeat

[ad_1] Humans can predict how machines (mis)classify adversarial images  VentureBeatWe question how AI could label an image of a dog as a pineapple. But research shows there's a logic to these...

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DeephESC 2.0: Deep Generative Multi Adversarial Networks for improving the classification of hESC.

[ad_1] Related Articles DeephESC 2.0: Deep Generative Multi Adversarial Networks for improving the classification of hESC. PLoS One. 2019;14(3):e0212849 Authors: Theagarajan R, Bhanu B Abstract Human embryonic stem cells (hESC),...

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Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning. (arXiv:1902.05644v1 [cs.AI])

[ad_1] We pose an active perception problem where an autonomous agent actively interacts with a second agent with potentially adversarial behaviors. Given the uncertainty in the intent of the other...

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Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks. (arXiv:1902.00577v1 [cs.LG])

[ad_1] Adversarial attacks and the development of (deep) neural networks robust against them are currently two widely researched topics. The robustness of Learning Vector Quantization (LVQ) models against adversarial attacks...

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An automatic diagnostic network using skew-robust adversarial discriminative domain adaptation to evaluate the severity of depression.

[ad_1] Related Articles An automatic diagnostic network using skew-robust adversarial discriminative domain adaptation to evaluate the severity of depression. Comput Methods Programs Biomed. 2019 Jan 17;: Authors: Sun B, Zhang...

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Generative Adversarial Classifier for Handwriting Characters Super-Resolution. (arXiv:1901.06199v1 [cs.CV])

[ad_1] Generative Adversarial Networks (GAN) receive great attentions recently due to its excellent performance in image generation, transformation, and super-resolution. However, GAN has rarely been studied and trained for classification,...

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Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning based Registration.

[ad_1] Related Articles Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning based Registration. Med Image Comput Comput Assist Interv. 2018 Sep;11070:739-746 Authors: Fan J, Cao X, Xue Z,...

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[Applications of generative adversarial networks in medical image processing].

[ad_1] Related Articles [Applications of generative adversarial networks in medical image processing]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2018 Jun 01;35(6):970-976 Authors: Pan D, Jia L, Zeng...

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SUSAN: segment unannotated image structure using adversarial network.

[ad_1] Related Articles SUSAN: segment unannotated image structure using adversarial network. Magn Reson Med. 2018 Dec 10;: Authors: Liu F Abstract PURPOSE: To describe and evaluate a segmentation method using...

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