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A Note on the Inception Score
Shane Barratt,Rishi Sharma +1 more
TLDR
New insights are provided into the Inception Score, a recently proposed and widely used evaluation metric for generative models, and it is demonstrated that it fails to provide useful guidance when comparing models.Abstract:
Deep generative models are powerful tools that have produced impressive results in recent years. These advances have been for the most part empirically driven, making it essential that we use high quality evaluation metrics. In this paper, we provide new insights into the Inception Score, a recently proposed and widely used evaluation metric for generative models, and demonstrate that it fails to provide useful guidance when comparing models. We discuss both suboptimalities of the metric itself and issues with its application. Finally, we call for researchers to be more systematic and careful when evaluating and comparing generative models, as the advancement of the field depends upon it.read more
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“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告
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Large Scale GAN Training for High Fidelity Natural Image Synthesis
TL;DR: BigGAN as mentioned in this paper applies orthogonal regularization to the generator, allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the generator's input, leading to models which set the new state of the art in class-conditional image synthesis.
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Which Training Methods for GANs do actually Converge
TL;DR: This paper describes a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent, and extends convergence results to more general GANs and proves local convergence for simplified gradient penalties even if the generator and data distribution lie on lower dimensional manifolds.
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Generating Diverse High-Fidelity Images with VQ-VAE-2
TL;DR: It is demonstrated that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity.
Proceedings ArticleDOI
DeepRoad: GAN-based metamorphic testing and input validation framework for autonomous driving systems
TL;DR: The experimental results demonstrate that DeepRoad can detect thousands of inconsistent behaviors for DNN-based autonomous driving systems, and effectively validate input images to potentially enhance the system robustness as well.
References
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Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Journal ArticleDOI
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Proceedings ArticleDOI
Rethinking the Inception Architecture for Computer Vision
TL;DR: In this article, the authors explore ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.
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Rethinking the Inception Architecture for Computer Vision
TL;DR: This work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.