Mimicry: Towards the Reproducibility of GAN Research

Abstract

Advancing the state of Generative Adversarial Networks (GANs) research requires one to make careful and accurate comparisons with existing works. Yet, this is often difficult to achieve in practice when models are often implemented differently using varying frameworks, and evaluated using different procedures even when the same metric is used. To mitigate these issues, we introduce Mimicry, a lightweight PyTorch library that provides implementations of popular state-of-the-art GANs and evaluation metrics to closely reproduce reported scores in the literature. We provide comprehensive baseline performances of different GANs on seven widely-used datasets by training these GANs under the same conditions, and evaluating them across three popular GAN metrics using the same procedures. The library can be found at https://github.com/kwotsin/mimicry.

arXiv commentAccepted to the AI for Content Creation Workshop at CVPR 2020

References in corpus (13)

• Adam: A Method for Stochastic Optimization
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Spectral Normalization for Generative Adversarial Networks
• LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
• cGANs with Projection Discriminator
• A Note on the Inception Score
• DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
• Demystifying MMD GANs
• Video-to-Video Synthesis
• A domain agnostic measure for monitoring and evaluating GANs
• RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis
• Few-shot Video-to-Video Synthesis

Cited by in corpus (9)

• medigan: a Python library of pretrained generative models for medical image synthesis
• Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks
• SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains
• GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks
• Slimmable Generative Adversarial Networks
• Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers
• F-Drop&Match: GANs with a Dead Zone in the High-Frequency Domain
• Generative Model without Prior Distribution Matching
• Multiclass non-Adversarial Image Synthesis, with Application to Classification from Very Small Sample