Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Instance-Conditioned GAN   Generative Adversarial Networks (GANs) can generate near photo realistic
images in narrow domains such as human faces. Yet, modeling complex
distributions of datasets such as ImageNet and COCO-Stuff remains challenging
in unconditional settings. In this paper, we take inspiration from kernel
density estimation techniques and introduce a non-parametric approach to
modeling distributions of complex datasets. We partition the data manifold into
a mixture of overlapping neighborhoods described by a datapoint and its nearest
neighbors, and introduce a model, called instance-conditioned GAN (IC-GAN),
which learns the distribution around each datapoint. Experimental results on
ImageNet and COCO-Stuff show that IC-GAN significantly improves over
unconditional models and unsupervised data partitioning baselines. Moreover, we
show that IC-GAN can effortlessly transfer to datasets not seen during training
by simply changing the conditioning instances, and still generate realistic
images. Finally, we extend IC-GAN to the class-conditional case and show
semantically controllable generation and competitive quantitative results on
ImageNet; while improving over BigGAN on ImageNet-LT. Code and trained models
to reproduce the reported results are available at
https://github.com/facebookresearch/ic_gan.

9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con