ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations

Latest segmentation and detection approaches (DeepLabV3+, FasterRCNN) applied to street fashion images. Arxiv paper contains information about both: net and dataset.

Arxiv link: https://arxiv.org/abs/1807.01394
Paperdoll dataset: http://vision.is.tohoku.ac.jp/~kyamagu/research/paperdoll/

#segmentation #dataset #fashion #sv

Hey, our fellow colleagues at OpenDataScience community are labeling a meme dataset. You can help them with the markup just by viewing memes in this bot: @MemezoidBot

#DataSet #labeling

Neural scene representation and rendering

In June #DeepMind introduced Generative Query Network (#GQN) framework within which machines learn to perceive their surroundings by training only on data obtained by themselves as they move around scenes.

Link: https://deepmind.com/blog/neural-scene-representation-and-rendering/

#DeepMind new release: Neural Processes (#NPs) that generalise #GQN ’s training regime to other few-shot prediction tasks such as regression and classification

Arxiv 1: https://arxiv.org/abs/1807.01622
Arxiv 2: https://arxiv.org/abs/1807.01613

#ICML2018

How #Netflix used data science to make scripts for the shows:

Video: https://www.youtube.com/watch?v=qXo9jTxfqJ8&feature=youtu.be
Github: http://netflix.github.io

Really great video, showing practical approach, with some focus on human interactions and integrating data insights into the product.

#youtube #netflixresearch

Udacity has published a github repo for the Deep Reinforcement Learning Nanodegree program

Repo: https://github.com/udacity/deep-reinforcement-learning
Nanodegree: https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893

#dl #udacity #mooc #course #github #rl

Deep Learning for Matching in Search and Recommendation

PDF: http://www.comp.nus.edu.sg/~xiangnan/sigir18-deep.pdf

#sigir2018 #Tutorial

Glow by #OpenAI: Better Reversible Generative Models

Project link: https://blog.openai.com/glow/
Video: https://d4mucfpksywv.cloudfront.net/research-covers/glow/videos/both_loop_new.mp4

Dynamic few-shot visual learning without forgetting by Gidaris and Komodakis

The authors share even configs and learning rate schedules for experiments in paper.

Github: https://github.com/gidariss/FewShotWithoutForgetting

#cvpr2018

SwitchNorm: add BatchNorm + InstanceNorm + GroupNorm with a learnable blend at each layer.

Paper about optimal normalization in neural nets continues. Plots + code

Arxiv: https://arxiv.org/abs/1806.10779

#dl #normalization

And it would be a crime to leave #ICML2018 out.

It is held in Stockholm now, papers are available at: http://proceedings.mlr.press/v80/

#conference

Toward theoretical understanding of deep learning

http://unsupervised.cs.princeton.edu/deeplearningtutorial.html

#ICML2018 #tutorial #dl

Facebook Research at ICML 2018

Seventeen papers by #FAIR at the #ICML2018

https://research.fb.com/facebook-research-at-icml-2018/

#Nvidia developed approach for cleaning noisy photoes. This comes extremely handy in the radiology.

Link: https://news.developer.nvidia.com/ai-can-now-fix-your-grainy-photos-by-only-looking-at-grainy-photos/?ncid=--45511

#dl #cv

ICML 2018 Accepted Papers Stats

Medium post with the meta statistics about accepted papers.

https://medium.com/machine-learning-in-practice/icml-2018-accepted-papers-stats-339f1cad5a55

#ICML2018 #meta #statistics

Visitor's highlights on #ICML2018

[ICML 2018] Day 2 - Representation Learning, Networks and Relational Learning

https://gmarti.gitlab.io/ml/2018/07/11/icml18-day-2.html

Ian Goodfellow (Google Brain) will have a free webinar on Adversarial Machine Learning

https://event.on24.com/eventRegistration/EventLobbyServlet?target=reg20.jsp&partnerref=twitterShareFromReg&ms=1531251848082&eventid=1633807&sessionid=1&key=8B7A8F4B65B54C35752F8A6FE23F641A&regTag=&sourcepage=register

#webinar #google #gbrain