Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CogView: Mastering Text-to-Image Generation via Transformers   Text-to-Image generation in the general domain has long been an open problem,
which requires both a powerful generative model and cross-modal understanding.
We propose CogView, a 4-billion-parameter Transformer with VQ-VAE tokenizer to
advance this problem. We also demonstrate the finetuning strategies for various
downstream tasks, e.g. style learning, super-resolution, text-image ranking and
fashion design, and methods to stabilize pretraining, e.g. eliminating NaN
losses. CogView achieves the state-of-the-art FID on the blurred MS COCO
dataset, outperforming previous GAN-based models and a recent similar work
DALL-E.

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The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin