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Weekly Research Newsletter - KIIT - Issue #11

Weekly Research Newsletter - KIIT - Issue #11
14th May 2021,
Really sorry for the month long delay, end semester exams really threw us off schedule.
We are excited to share this week’s picks for the research newsletter. We hope you’ll enjoy reading them over the weekend.

Enhancing Photorealism Enhancement
Stephan R. Richter, Hassan Abu AlHaija, Vladlen Koltun
We present an approach to enhancing the realism of synthetic images. The images are enhanced by a convolutional network that leverages intermediate representations produced by conventional rendering pipelines. The network is trained via a novel adversarial objective, which provides strong supervision at multiple perceptual levels. We analyze scene layout distributions in commonly used datasets and find that they differ in important ways. We hypothesize that this is one of the causes of strong artifacts that can be observed in the results of many prior methods. To address this we propose a new strategy for sampling image patches during training. We also introduce multiple architectural improvements in the deep network modules used for photorealism enhancement. We confirm the benefits of our contributions in controlled experiments and report substantial gains in stability and realism in comparison to recent image-to-image translation methods and a variety of other baselines.
Diffusion Models Beat GANs on Image Synthesis
Prafulla Dhariwal, Alex Nichol
We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for sample quality using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128×128, 4.59 on ImageNet 256×256, and 7.72 on ImageNet 512×512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.85 on ImageNet 512×512. We release our code at this https URL
Did you enjoy this issue?
Priyansi, Junaid Rahim and Biswaroop Bhattacharjee

An opt-in weekly newsletter for the undergraduate research enthusiasts in KIIT. We intend to share interesting research articles and start conversations about the latest ideas in artificial intelligence, computer science and mathematics.

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