Synthesis Blog

Together, we’re building the future of computer vision and machine learning.
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The Creativity Scale: Can AI Do Science?

Today, I want to discuss two recently developed AI systems that can help with one of the holy grails of AI: doing research automatically. Google’s AI Co-Scientist appears to be a tireless research partner that can read thousands of papers overnight and brainstorm ideas with you… actually, it can brainstorm ideas internally and give you only the best of the best. Sakana AI’s AI Scientist-v2 doesn’t need you at all, it just writes new papers from scratch, and its papers are getting accepted to some very good venues. To contextualize these novelties, I also want to discuss where current AI models are, creatively speaking—and what this question means, exactly.

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March 21, 2025

Today, I want to discuss two recently developed AI systems that can help with one of the holy grails of…

February 25, 2025

Some of the most important AI advances in 2024 were definitely test-time reasoning LLMs, or large reasoning models (LRM), that…

January 28, 2025

We interrupt your regularly scheduled programming to discuss a paper released on New Year’s Eve: on December 31, 2024, Google…

January 17, 2025

It is time to discuss some applications. Today, I begin with using LLMs for programming. There is at least one…

November 20, 2024

We have already discussed how to extend the context size for modern Transformer architectures, but today we explore a different…

November 7, 2024

Although deep learning is a very new branch of computer science, foundations of neural networks have been in place since…

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CVPR ‘22, Part I: New Synthetic Datasets

CVPR 2022, the largest and most prestigious conference in computer vision and one of the most important ML venues in general, has just finished in New Orleans. With over 2000 accepted papers, reviewing the contributions of this year’s CVPR appears to be a truly gargantuan task. Over the next series of blog posts, we will attempt to go over the most interesting papers directly related to our main topic: synthetic data. Today, I present the first but definitely not the last installment devoted to papers from CVPR 2022.

CVPR ‘22, Part II: New Use Cases for Synthetic Data

Last time, we started a new series of posts: an overview of papers from CVPR 2022 that are related to synthetic data. This year’s CVPR has over 2000 accepted papers, and many of them touch upon our main topic on this blog. In today’s installment, we look at papers that make use of synthetic data to advance a number of different use cases in computer vision, along with a couple of very interesting and novel ideas that extend the applicability of synthetic data in new directions. We will even see some fractals as synthetic data! (image source)

CVPR ‘22, Part III: Digital Humans

Last time, we talked about new use cases for synthetic data, from crowd counting to fractal-based synthetic images for pretraining large models. But there is a large set of use cases that we did not talk about, united by their relation to digital humans: human avatars, virtual try-on for clothes, machine learning for improving animations in synthetic humans, and much more. Today, we talk about the human side of CVPR 2022, considering two primary applications: conditional generation for applications such as virtual try-on and learning 3D avatars from 2D images (image generated by DALL-E-Mini by craiyon.com with the prompt “virtual human in the metaverse”).