  StyleGANs are at the forefront of controllable image generation as they
produce a latent space that is semantically disentangled, making it suitable
for image editing and manipulation. However, the performance of StyleGANs
severely degrades when trained via class-conditioning on large-scale
long-tailed datasets. We find that one reason for degradation is the collapse
of latents for each class in the $\mathcal{W}$ latent space. With NoisyTwins,
we first introduce an effective and inexpensive augmentation strategy for class
embeddings, which then decorrelates the latents based on self-supervision in
the $\mathcal{W}$ space. This decorrelation mitigates collapse, ensuring that
our method preserves intra-class diversity with class-consistency in image
generation. We show the effectiveness of our approach on large-scale real-world
long-tailed datasets of ImageNet-LT and iNaturalist 2019, where our method
outperforms other methods by $\sim 19\%$ on FID, establishing a new
state-of-the-art.
