Feature Quantization Improves GAN Training

The instability in GAN training has been a long-standing problem despite remarkable research efforts. We identify that instability issues stem from difficulties of performing feature matching with mini-batch statistics, due to a fragile balance between the fixed target distribution and the progressively generated distribution. In this work, we propose Feature Quantization (FQ) for the discriminator, to embed both true and fake data samples into a shared discrete space. The quantized values of FQ are constructed as an evolving dictionary, which is consistent with feature statistics of the recent distribution history. Hence, FQ implicitly enables robust feature matching in a compact space. Our method can be easily plugged into existing GAN models, with little computational overhead in training. We apply FQ to 3 representative GAN models on 9 benchmarks: BigGAN for image generation, StyleGAN for face synthesis, and U-GAT-IT for unsupervised image-to-image translation. Extensive experimental results show that the proposed FQ-GAN can improve the FID scores of baseline methods by a large margin on a variety of tasks, achieving new state-of-the-art performance.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Image-to-Image Translation anime-to-selfie FQ-GAN Kernel Inception Distance 10.23 # 1
Conditional Image Generation CIFAR-10 FQ-GAN Inception score 8.50 # 12
FID 5.34 # 5
Conditional Image Generation CIFAR-100 FQ-GAN Inception Score 9.74 # 3
FID 7.15 # 1
Image Generation FFHQ 1024 x 1024 FQ-GAN FID 3.19 # 9
Conditional Image Generation ImageNet 128x128 FQ-GAN FID 13.77 # 17
Inception score 54.36 # 16
Conditional Image Generation ImageNet 64x64 FQ-GAN FID 9.67 # 3
Inception score 25.96 # 4
Image-to-Image Translation selfie-to-anime FQ-GAN Kernel Inception Distance 11.40 # 1

Methods