Tokyo Olympics Alternative medals table

Article on how teams performed with the respect to behavior expected by regression model.

Link: https://ig.ft.com/tokyo-olympics-alternative-medal-table/

​​Virtual fitting room launched by our friends

#in3D launched a 3D virtual fitting room with Replicant Fashion house. 30+ designers, 60+ looks.

Great example of the AI-driven product!

Desktop: https://www.replicant.fashion/digitaltwin
iPhone: https://apps.apple.com/us/app/in3d-3d-body-scanning/id1467153183

#aiproduct #fitting #metaverse

​​Domain-Aware Universal Style Transfer

Style transfer aims to reproduce content images with the styles from reference images. Modern style transfer methods can successfully apply arbitrary styles to images in either an artistic or a photo-realistic way. However, due to their structural limitations, they can do it only within a specific domain: the degrees of content preservation and stylization depends on a predefined target domain. As a result, both photo-realistic and artistic models have difficulty in performing the desired style transfer for the other domain.

The authors propose Domain-aware Style Transfer Networks (DSTN) that transfer not only the style but also the property of domain (i.e., domainness) from a given reference image. Furthermore, they design a novel domainess indicator (based on the texture and structural features) and introduce a unified framework with domain-aware skip connection to adaptively transfer the stroke and palette to the input contents guided by the domainness indicator.

Extensive experiments validate that their model produces better qualitative results and outperforms previous methods in terms of proxy metrics on both artistic and photo-realistic stylizations.

Paper: https://arxiv.org/abs/2108.04441

Code: https://github.com/Kibeom-Hong/Domain-Aware-Style-Transfer

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-dstn

#deeplearning #cv #styletransfer

14 seconds of April #Nvidia 's CEO speech was generated in silico

Why this important: demand for usage of 3080 and newer GPU models might also get pumped by CGI artists and researchers working in VR / AR tech.

And this raises the bar for #speechsinthesis / #speechgeneration and definately for the rendering of photorealistic picture.

YouTube making of video: https://www.youtube.com/watch?v=1qhqZ9ECm70&t=1430s
Vice article on the subject: https://www.vice.com/en/article/88nbpa/nvidia-reveals-its-ceo-was-computer-generated-in-keynote-speech

​​Program Synthesis with Large Language Models

Paper compares models used for program synthesis in general purpose programming languages against two new benchmarks, MBPP (The Mostly Basic Programming Problems) and MathQA-Python, in both the few-shot and fine-tuning regimes.

MBPP contains 974 programming tasks, designed to be solvable by entry-level programmers. MathQA benchmark, contains 23914 problems that evaluate the ability of the models to synthesize code from more complex text.

Largest fine-tuned model achieves 83.8 percent accuracy on the latter benchmark.

Why this is interesting: better models for code / problem understanding means improved search for the coding tasks and the improvement of the coding-assistant projects like #TabNine or #Copilot

ArXiV: https://arxiv.org/abs/2108.07732

#DL #NLU #codewritingcode #benchmark

Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity

#Baidu research proposed a structure-aware interactive graph neural network ( #SIGN ) to better learn representations of protein-ligand complexes, since drug discovery relies on the successful prediction of protein-ligand binding affinity.

Link: https://dl.acm.org/doi/10.1145/3447548.3467311

#biolearning #deeplearning

​​⚡️Breeaking news!

Big project, first public release! From the creator of FastAPI and Typer: SQLModel.

SQLModel is a library for interacting with SQL databases from Python code, with Python objects. It is designed to be intuitive, easy to use, highly compatible, and robust.

SQLModel is based on Python type annotations, and powered by Pydantic and SQLAlchemy.
SQLModel is, in fact, a thin layer on top of Pydantic and SQLAlchemy, carefully designed to be compatible with both.

The key features are:
- Intuitive to write: Great editor support. Completion everywhere. Less time debugging. Designed to be easy to use and learn. Less time reading docs.
- Easy to use: It has sensible defaults and does a lot of work underneath to simplify the code you write.
- Compatible: It is designed to be compatible with FastAPI, Pydantic, and SQLAlchemy.
- Extensible: You have all the power of SQLAlchemy and Pydantic underneath.
- Short: Minimize code duplication. A single type annotation does a lot of work. No need to duplicate models in SQLAlchemy and Pydantic.

https://github.com/tiangolo/sqlmodel

New German V4 Model and English V5 Models

New and improved models in Silero-models! Community edition versions available here: https://github.com/snakers4/silero-models

Huge performance improvements for two new models:

- English V5 (quality)
- German V3 (quality)

The models currently are available in the following flavors:

- English V5 jit (small), onnx (small), jit_q (small, quantized), jit_xlarge, onnx_xlarge
- German V3 jit_large, onnx_large

The xsmall model family for English in on the way.

The quality growth visualization:

​​iRobot with poop detection

iRobot (company building cleaning house robots) had a problem with robots regarding pet poops. So they built a special model along with physical models of poop to test the product.

iRobot official YouTube: https://www.youtube.com/watch?v=2rj3VUmRNnU
TechCrunch: https://techcrunch.com/2021/09/09/actuator-4/

#aiproduct #marketinggurus

New attempt at proving P≠NP

Martin Dowd published a 5-page paper claiming to contain a proof that P ≠ NP. This is a fundamental question, comparing quickly checkable against quickly solvalble problems.

Basically, proving P != NP would mean that there will be unlimited demand alphago-like solutions in different spheres, because that will mean (as a scientific fact) that there are problems not having fast [enough] analytical solutions.

ResearchGate: https://www.researchgate.net/publication/354423778_P_Does_Not_Equal_NP
Wiki on the problem: https://en.wikipedia.org/wiki/P_versus_NP_problem

#fundamental #pnenp #computerscience

​​Counting Happiness and Where it Comes From

Researches asked 10 000 Mechanical Turk participants to name 10 things which are making them happy, resulting in creation of HappyDB.

And since that DB is open, Nathan Yau analyzed and vizualized this database in the perspective of subjects and actions, producing intersting visualization.

Hope that daily reading @opendatascience makes you at least content, if not happy.

Happines reason visualization link: https://flowingdata.com/2021/07/29/counting-happiness
HappyDB link: https://megagon.ai/projects/happydb-a-happiness-database-of-100000-happy-moments/

#dataset #emotions #visualization

​​SwinIR: Image Restoration Using Swin Transformer

Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy, and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers, which show impressive performance on high-level vision tasks.

The authors use a model SwinIR based on the Swin Transformers. Experimental results demonstrate that SwinIR outperforms state-of-the-art methods on different tasks (image super-resolution, image denoising, and JPEG compression artifact reduction) by up to 0.14~0.45dB, while the total number of parameters can be reduced by up to 67%.

Paper: https://arxiv.org/abs/2108.10257
Code: https://github.com/JingyunLiang/SwinIR

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-swinir

#deeplearning #cv #transformer #superresolution #imagerestoration

​​Summarizing Books with Human Feedback

#OpenAI fine-tuned #GPT3 to summarize books well enough to be human-readable. Main approach: recursively split text into parts and then meta-summarize summaries.

This is really important because once there will be a great summarization #SOTA we won't need editors to write posts for you. And researchers ultimatively will have some asisstance interpreting models' results.

BlogPost: https://openai.com/blog/summarizing-books/
ArXiV: https://arxiv.org/abs/2109.10862

#summarization #NLU #NLP

​​AI Generated Pokemon Sprites with GPT-2

Author trained #GPT2 model to generate #pokemon sprites, encoding them as the lines of characters (including color). Surprisingly, results were decent, so this leaves us wonder if #GPT3 results would be better.

YouTube: https://www.youtube.com/watch?v=Z9K3cwSL6uM
GitHub: https://github.com/MatthewRayfield/pokemon-gpt-2
Article: https://matthewrayfield.com/articles/ai-generated-pokemon-sprites-with-gpt-2/
Example: https://matthewrayfield.com/projects/ai-pokemon/

#NLU #NLP #generation #neuralart

​​This Olesya doesn't exist

Author trained StyleGAN2-ADA network on 2445 personal photos to generate new photo on the site each time there is a refresh or click.


Website: https://thisolesyadoesnotexist.glitch.me
Olesya's personal site: https://monolesan.com

#StyleGAN2 #StyleGAN2ADA #generation #thisXdoesntexist

​​Real numbers, data science and chaos: How to fit any dataset with a single parameter

Gentle reminder that measure of information is bit and that single parameter can contain more information than multiple parameters.

ArXiV: https://arxiv.org/abs/1904.12320

#cs #bits #math

Interesting idea for using GitHub panes for data #visualization

Source: https://twitter.com/levelsio/status/1443133071230791680
Live: https://nomadlist.com/open

RoBERTa English Toxicity Classifier

We have released our fine-tuned RoBERTa based toxicity classifier for English language on🤗:
https://huggingface.co/SkolkovoInstitute/roberta_toxicity_classifier

The model was trained on the merge of the English parts of the three datasets by Jigsaw. The classifiers perform closely on the test set of the first Jigsaw competition, reaching the AUC-ROC of 0.98 and F1-score of 0.76.
So, you can use it now conveniently for any of your research or industrial tasks☺️