مقاله‌ی جالب کمپانی اپل!
( Submitted for review to a conference on Nov 15, 2016)

✏️Title:
Learning from Simulated and Unsupervised Images through Adversarial Training

✏️abstract:
With recent progress in graphics, it has become more tractable to train models on #synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulator's output using #unlabeled real data, while preserving the annotation information from the simulator. We develop a method for S+U learning that uses an #adversarial network similar to #Generative Adversarial Networks (#GANs), but with synthetic images as inputs instead of random vectors. We make several key modifications to the standard GAN algorithm to preserve annotations, avoid artifacts and stabilize training: (i) a 'self-regularization' term, (ii) a local adversarial loss, and (iii) updating the discriminator using a history of refined images. We show that this enables generation of highly realistic images, which we demonstrate both qualitatively and with a user study. We quantitatively evaluate the generated images by training models for gaze estimation and hand pose estimation. We show a significant improvement over using synthetic images, and achieve state-of-the-art results on the MPIIGaze dataset without any labeled real data.

🔗https://arxiv.org/abs/1612.07828v1
🔗https://arxiv.org/pdf/1612.07828v1.pdf

#unlabeled_data #unsupervised #unsupervised_learning #Generative #Generative_Models

Deep Learning 2016: The Year in Review

http://www.deeplearningweekly.com/blog/deep-learning-2016-the-year-in-review

✔️ #Unsupervised and #Reinforcement Learning
✔️ Deep Reinforcement Learning
✔️ #Generative Models
✔️ Continued Openness in AI development
✔️ Partnerships & Acquisitions
✔️ Hardware & Chips

(by Jan Bussieck on December 31, 2016)

In order to understand trends in the field, I find it helpful to think of developments in #deep_learning as being driven by three major frontiers that limit the success of #artificial_intelligence in general and deep learning in particular. Firstly, there is the available #computing power and #infrastructure, such as fast #GPUs, cloud services providers (have you checked out Amazon's new #EC2 P2 instance ?) and tools (#Tensorflow, #Torch, #Keras etc), secondly, there is the amount and quality of the training data and thirdly, the algorithms (#CNN, #LSTM, #SGD) using the training data and running on the hardware. Invariably behind every new development or advancement, lies an expansion of one of these frontiers.
...

Generative Adversarial Denoising Autoencoder for #Face Completion
pic: http://www.cc.gatech.edu/~hays/7476/projects/Avery_Wenchen/images/one.png

🔗http://www.cc.gatech.edu/~hays/7476/projects/Avery_Wenchen/
#GAN
#Generative #adversarial #Generative_Models #Autoencoder

The Alien Style of Deep Learning #Generative Design
https://medium.com/intuitionmachine/the-alien-look-of-deep-learning-generative-design-5c5f871f7d10

[Dec 25, 2016, 3min read]


طراحی های بیگانه، عجیب و کارا توسط یادگیری ژرف!

با استفاده از generative modelها ایده های جالبی برای طراحی های صنعتی یا حتی ایده ی دکوراسیون منزل خروجی بگیرید!

همچنین توانسته یک شبکه ی lstm بهینه را بسازد (کاربرد متا مدل)

این روش به طراحان اجازه می دهد به اهداف طراحی ورودی خاص، از جمله الزامات عملکردی، نوع مواد، روش ساخت، معیارهای عملکرد و محدودیت های هزینه را به عنوان ورودی بدهند.
سیستم پس از ایجاد طرح های مختلف و جستجو در بین طرح های خلق شده، بر اساس نیازمندی های ذکر شده بهترین طرح های پیشنهادی را خروجی میدهد!

#Alien_Style #GAN

سورس کد تکمیل نواحی از دست رفته ی تصویر با فریم ورک #تنسورفلو به همراه توضیحات کامل
Image #Completion with Deep Learning in #TensorFlow

🔗blog post: http://bamos.github.io/2016/08/09/deep-completion/
🔗source code: https://github.com/bamos/dcgan-completion.tensorflow

مطالب مرتبط:
https://t.me/cvision/75
https://arxiv.org/abs/1607.07539

#GAN #Generative_Adversarial #DCGAN

img: http://bit.ly/2oQJgwo

نرم افزار فیس‌اپ که اخیرا محبوبیت زیادی پیدا کرده, از یادگیری عمیق و به طور خاص
Deep generative convolutional neural network
استفاده میکند

اطلاعات بیشتر:
https://techcrunch.com/2017/02/08/faceapp-uses-neural-networks-for-photorealistic-selfie-tweaks/

لینک دانلود نرم افزار فیس اپ:
Android version: http://bit.ly/2pqO4f9
IOS version: http://apple.co/2qejHWq

#deep_learning #generative #convolutional #neural_network

Multi-style #Generative Network for #Real_time Transfer
Paper:
🔗 https://arxiv.org/abs/1703.06953
Code:
🔗 https://github.com/zhanghang1989/MSG-Net

#Style_Transfer #deep_learning #GAN #MSG_Net #CNN

Generating Videos with Scene Dynamics
video: http://bit.ly/2q6THM9

تبدیل تصویر به فیلم.
هوش مصنوعی ای که قادر است تنها با یک تصویر ثابت، فیلم چند ثانیه ای حاوی حرکت خروجی دهد...

در این روش به صورت بدون ناظر دو سال ویدیوی جمع آوری از فلیکر به شبکه آموزش داده شده است، سپس شبکه توانسته که نگاشتی از تصاویر به فیلم های چند ثانیه ای ایجاد کند.

🔗 http://web.mit.edu/vondrick/tinyvideo/

#generative #adversarial #GAN #deep_learning

#مقاله
✔️ایجاد یک نگاشت از تصور به تصویر:
در این کار شبکه های شرطی در مقابل حریف (GAN) آموزش دیده اند که یک نگاشت از تصویر ورودی به تصویر خروجی بیابند...


Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
[UC Berkeley] pic: http://bit.ly/2s2OTsm

🔗abstract:
https://arxiv.org/abs/1703.10593

🔗Paper:
https://arxiv.org/pdf/1703.10593.pdf

🔗Project Page:
https://junyanz.github.io/CycleGAN/

🔗codes:
https://github.com/junyanz/CycleGAN
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix


Our goal is to learn a mapping G: X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss.

مرتیط به مقاله ی:
https://t.me/cvision/171

#CycleGAN #GAN #Generative #CNN #Convolutional #deep_learning #adversarial #Generative_Models #Generative

تبدیل اسب به گورخر!
ایجاد نگاشت تصویر به تصویر توسط هوش مصنوعی...

اطلاعات بیشتر:
https://t.me/cvision/214

#CycleGAN #GAN #Generative #CNN #Convolutional #deep_learning #adversarial #generative

#آموزش در سایت ماکروسافت
Learning Image to Image Translation with CycleGANs
[Published June 12, 2017]
https://www.microsoft.com/reallifecode/2017/06/12/learning-image-image-translation-cyclegans/

مرتبط با https://t.me/cvision/214

#CycleGAN #GAN #Generative #CNN #Convolutional #deep_learning #adversarial #Generative_Models #Generative

#مقاله #سورس_کد
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
+ PyTorch Implementation of StarGAN - #CVPR_2018


🔗abstract:
https://arxiv.org/abs/1711.09020
🔗Paper:
https://arxiv.org/pdf/1711.09020.pdf
🔗Code:
https://github.com/yunjey/StarGAN
🎬Video Demo:
https://www.youtube.com/watch?v=EYjdLppmERE


#GAN #stargan #pytorch #generative #adversarial

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
+ کد
اطلاعات بیشتر

https://t.me/cvision/646

#GAN #stargan #pytorch #generative #adversarial

#دمو #مقاله #سورس_کد
دموی آنلاین تبدیل چهره با شبکه های مولد

https://blog.openai.com/glow/

#GAN #GLOW #generative

#مقاله ٫ #سورس_کد کد #دیتاست

https://t.me/cvision/668

Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"


🔗abstract:
https://arxiv.org/abs/1807.03039

🔗Paper:
https://arxiv.org/pdf/1807.03039

🔗source code:
https://github.com/openai/glow

🔗Project Page + Online #Demo:
https://blog.openai.com/glow/

#Tensorflow #Horovod #dataset #gan #GLOW #generative