A pixel to-pixel segmentation method of DILD without masks using CNN and perlin noise
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A pixel to-pixel segmentation method of diffuse interstitial lung disease images without human masks using deep convolution networks and perlin noise
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Convolutional neural network in practice
Convolutional neural network in practice남주 김 Practical guide of state-of-the-art convolutional neural network technology for deep learning engineers.
Finding connections among images using CycleGAN
Finding connections among images using CycleGANNAVER Engineering 발표자: 박태성 (UC Berkeley 박사과정)
발표일: 2017.6.
Taesung Park is a Ph.D. student at UC Berkeley in AI and computer vision, advised by Prof. Alexei Efros.
His research interest lies between computer vision and computational photography, such as generating realistic images or enhancing photo qualities. He received B.S. in mathematics and M.S. in computer science from Stanford University.
개요:
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs.
However, for many tasks, paired training data will not be available.
We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples.
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.
Because this mapping is highly under-constrained, we couple it with an inverse mapping F: Y → X and introduce a cycle consistency loss to push F(G(X)) ≈ X (and vice versa).
Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc.
Quantitative comparisons against several prior methods demonstrate the superiority of our approach.
1시간만에 GAN(Generative Adversarial Network) 완전 정복하기
1시간만에 GAN(Generative Adversarial Network) 완전 정복하기NAVER Engineering 발표자: 최윤제(고려대 석사과정)
최윤제 (Yunjey Choi)는 고려대학교에서 컴퓨터공학을 전공하였으며, 현재는 석사과정으로 Machine Learning을 공부하고 있는 학생이다. 코딩을 좋아하며 이해한 것을 다른 사람들에게 공유하는 것을 좋아한다. 1년 간 TensorFlow를 사용하여 Deep Learning을 공부하였고 현재는 PyTorch를 사용하여 Generative Adversarial Network를 공부하고 있다. TensorFlow로 여러 논문들을 구현, PyTorch Tutorial을 만들어 Github에 공개한 이력을 갖고 있다.
개요:
Generative Adversarial Network(GAN)은 2014년 Ian Goodfellow에 의해 처음으로 제안되었으며, 적대적 학습을 통해 실제 데이터의 분포를 추정하는 생성 모델입니다. 최근 들어 GAN은 가장 인기있는 연구 분야로 떠오르고 있고 하루에도 수 많은 관련 논문들이 쏟아져 나오고 있습니다.
수 없이 쏟아져 나오고 있는 GAN 논문들을 다 읽기가 힘드신가요? 괜찮습니다. 기본적인 GAN만 완벽하게 이해한다면 새로 나오는 논문들도 쉽게 이해할 수 있습니다.
이번 발표를 통해 제가 GAN에 대해 알고 있는 모든 것들을 전달해드리고자 합니다. GAN을 아예 모르시는 분들, GAN에 대한 이론적인 내용이 궁금하셨던 분들, GAN을 어떻게 활용할 수 있을지 궁금하셨던 분들이 발표를 들으면 좋을 것 같습니다.
발표영상: https://youtu.be/odpjk7_tGY0
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Reading group gan - 20170417
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Starting from the very basic of what a GAN is, passing trough Tensorflow implementation, using the most cutting-edge APIs available in the framework, and finally, production-ready serving at scale using Google Cloud ML Engine.
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그림 그리는 AI
그림 그리는 AINAVER Engineering 발표자: 이활석 (Naver Clova)
발표일: 2017.11.
(현) NAVER Clova Vision
(현) TFKR 운영진
개요:
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨지고 있습니다.
특히 컴퓨터 비전 기술 분야에서는 지도학습에 해당하는 이미지 내에 존재하는 정보를 찾는 인식 기술에서,
비지도학습에 해당하는 특정 정보를 담는 이미지를 생성하는 기술인 생성 기술로 연구 동향이 바뀌어 가고 있습니다.
본 세미나에서는 생성 기술의 두 축을 담당하고 있는 VAE(variational autoencoder)와 GAN(generative adversarial network) 동작 원리에 대해서 간략히 살펴 보고, 관련된 주요 논문들의 결과를 공유하고자 합니다.
딥러닝에 대한 지식이 없더라도 생성 모델을 학습할 수 있는 두 방법론인 VAE와 GAN의 개념에 대해 이해하고
그 기술 수준을 파악할 수 있도록 강의 내용을 구성하였습니다.
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Tutorial on Theory and Application of Generative Adversarial Networks
Tutorial on Theory and Application of Generative Adversarial NetworksMLReview Description
Generative adversarial network (GAN) has recently emerged as a promising generative modeling approach. It consists of a generative network and a discriminative network. Through the competition between the two networks, it learns to model the data distribution. In addition to modeling the image/video distribution in computer vision problems, the framework finds use in defining visual concept using examples. To a large extent, it eliminates the need of hand-crafting objective functions for various computer vision problems. In this tutorial, we will present an overview of generative adversarial network research. We will cover several recent theoretical studies as well as training techniques and will also cover several vision applications of generative adversarial networks.
EuroSciPy 2019 - GANs: Theory and Applications
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GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production.
The workshop aims at providing a complete understanding of both the theory and the practical know-how to code and deploy this family of models in production. By the end of it, the attendees should be able to apply the concepts learned to other models without any issues.
We will be showcasing all the shiny new APIs introduced by TensorFlow 2.0 by showing how to build a GAN from scratch and how to "productionize" it by leveraging the AshPy Python package that allows to easily design, prototype, train and export Machine Learning models defined in TensorFlow 2.0.
The workshop is composed of:
- Theoretical introduction
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- High-performance input data pipeline with TensorFlow Datasets
- Introduction to the AshPy API
- Implementing, training, and visualizing DCGAN using AshPy
- Serving TF2 Models with Google Cloud Functions
The materials of the workshop will be openly provided via GitHub (https://github.com/zurutech/gans-from-theory-to-production).
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Generative Adversarial Networks
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youtube: https://www.youtube.com/playlist?list=PLeeHDpwX2Kj5Ugx6c9EfDLDojuQxnmxmU
Tutorial on Deep Generative Models
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ICASSP 2018 Tutorial: Generative Adversarial Network and its Applications to ...
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Unsupervised learning represenation with DCGAN
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(indico Research, Facebook AI Research).
Generative Adversarial Networks and Their Applications
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You can join Artifacia AI Meet Bangalore Group: https://www.meetup.com/Artifacia-AI-Meet/
Generative Adversarial Networks is an advanced topic and requires a prior basic understanding of CNNs. Here is some pre-reading material for you.
- https://arxiv.org/pdf/1406.2661v1.pdf
- https://arxiv.org/pdf/1701.00160v1.pdf
Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)
Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)Universitat Politècnica de Catalunya Slides by Víctor Garcia about:
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros, "Image-to-Image Translation Using Conditional Adversarial Networks".
In arxiv, 2016.
https://phillipi.github.io/pix2pix/
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.
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GAN in medical imagingCheng-Bin Jin The document summarizes a presentation on applying GANs in medical imaging. It discusses several papers on this topic:
1. A paper that used GANs to reduce noise in low-dose CT scans by training on paired routine-dose and low-dose CT images. This approach generated reconstructed low-dose CT images with improved quality.
2. A paper that used GANs for cross-modality synthesis, specifically generating skin lesion images from other modalities.
3. Additional papers discussed other medical imaging applications of GANs such as vessel-fundus image synthesis and organ segmentation.
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We implemented FCN, U-Net and Segnet Deep learning architectures for this task.
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Soumith Chintala is a Researcher at Facebook AI Research, where he works on deep learning, reinforcement learning, generative image models, agents for video games and large-scale high-performance deep learning. He holds a Masters in CS from NYU, and spent time in Yann LeCun's NYU lab building deep learning models for pedestrian detection, natural image OCR, depth-images among others.
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GAN - Theory and Applications
GAN - Theory and ApplicationsEmanuele Ghelfi GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production.
Starting from the very basic of what a GAN is, passing trough Tensorflow implementation, using the most cutting-edge APIs available in the framework, and finally, production-ready serving at scale using Google Cloud ML Engine.
Slides for the talk: https://www.pycon.it/conference/talks/deep-diving-into-gans-form-theory-to-production
Github repo: https://github.com/zurutech/gans-from-theory-to-production
Deep Generative Models
Deep Generative ModelsMijung Kim Deep generative models can be either generative or discriminative. Generative models directly model the joint distribution of inputs and outputs, while discriminative models directly model the conditional distribution of outputs given inputs. Common deep generative models include restricted Boltzmann machines, deep belief networks, variational autoencoders, generative adversarial networks, and deep convolutional generative adversarial networks. These models use different network architectures and training procedures to generate new examples that resemble samples from the training data distribution.
그림 그리는 AI
그림 그리는 AINAVER Engineering 발표자: 이활석 (Naver Clova)
발표일: 2017.11.
(현) NAVER Clova Vision
(현) TFKR 운영진
개요:
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨지고 있습니다.
특히 컴퓨터 비전 기술 분야에서는 지도학습에 해당하는 이미지 내에 존재하는 정보를 찾는 인식 기술에서,
비지도학습에 해당하는 특정 정보를 담는 이미지를 생성하는 기술인 생성 기술로 연구 동향이 바뀌어 가고 있습니다.
본 세미나에서는 생성 기술의 두 축을 담당하고 있는 VAE(variational autoencoder)와 GAN(generative adversarial network) 동작 원리에 대해서 간략히 살펴 보고, 관련된 주요 논문들의 결과를 공유하고자 합니다.
딥러닝에 대한 지식이 없더라도 생성 모델을 학습할 수 있는 두 방법론인 VAE와 GAN의 개념에 대해 이해하고
그 기술 수준을 파악할 수 있도록 강의 내용을 구성하였습니다.
Gan intro
Gan introHyungjoo Cho The document discusses different types of generative models including auto-regressive models, variational auto-encoders, and generative adversarial networks. It provides examples of each type of model and highlights some of their features and issues during training. Specific models discussed in more detail include PixelRNNs, DCGANs, WGANs, BEGANs, Pix2Pix, and CycleGANs. The document aims to introduce deep generative models and their applications.
Deep Advances in Generative Modeling
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Tutorial on Theory and Application of Generative Adversarial Networks
Tutorial on Theory and Application of Generative Adversarial NetworksMLReview Description
Generative adversarial network (GAN) has recently emerged as a promising generative modeling approach. It consists of a generative network and a discriminative network. Through the competition between the two networks, it learns to model the data distribution. In addition to modeling the image/video distribution in computer vision problems, the framework finds use in defining visual concept using examples. To a large extent, it eliminates the need of hand-crafting objective functions for various computer vision problems. In this tutorial, we will present an overview of generative adversarial network research. We will cover several recent theoretical studies as well as training techniques and will also cover several vision applications of generative adversarial networks.
EuroSciPy 2019 - GANs: Theory and Applications
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GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production.
The workshop aims at providing a complete understanding of both the theory and the practical know-how to code and deploy this family of models in production. By the end of it, the attendees should be able to apply the concepts learned to other models without any issues.
We will be showcasing all the shiny new APIs introduced by TensorFlow 2.0 by showing how to build a GAN from scratch and how to "productionize" it by leveraging the AshPy Python package that allows to easily design, prototype, train and export Machine Learning models defined in TensorFlow 2.0.
The workshop is composed of:
- Theoretical introduction
- GANs from Scratch in TensorFlow 2.0
- High-performance input data pipeline with TensorFlow Datasets
- Introduction to the AshPy API
- Implementing, training, and visualizing DCGAN using AshPy
- Serving TF2 Models with Google Cloud Functions
The materials of the workshop will be openly provided via GitHub (https://github.com/zurutech/gans-from-theory-to-production).
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Generative adversarial networks
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GANs and Applications
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ICASSP 2018 Tutorial: Generative Adversarial Network and its Applications to ...
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You can join Artifacia AI Meet Bangalore Group: https://www.meetup.com/Artifacia-AI-Meet/
Generative Adversarial Networks is an advanced topic and requires a prior basic understanding of CNNs. Here is some pre-reading material for you.
- https://arxiv.org/pdf/1406.2661v1.pdf
- https://arxiv.org/pdf/1701.00160v1.pdf
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Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros, "Image-to-Image Translation Using Conditional Adversarial Networks".
In arxiv, 2016.
https://phillipi.github.io/pix2pix/
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.
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Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)
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A pixel to-pixel segmentation method of DILD without masks using CNN and perlin noise
- 1. A pixel-to-pixel segmentation of DILD without masks using CNN and Perlin noise 2016.11 [email protected]
- 2. Objectives ● Segmenting and labeling regional patterns in DILD(Diffuse Interstitial Lung Disease) HRCT images. From : Younjun Chang et al, “Fast and efficient lung disease classification using hierarchical one-against-all SVM and cost-sensitive feature selection”. 2012.
- 3. Challenges ● Small dataset ○ only 547 ROI ( 20x20 bounding box ) patches ● No human mask label ○ Extremely expensive
- 4. Dataset
- 5. Dataset
- 6. Dataset
- 7. Dataset
- 8. Traditional approach ● Superpixel approach
- 9. Traditional approach ● Superpixel result - factor 0.25
- 10. Traditional approach ● Superpixel result - factor 2
- 11. Traditional approach ● Superpixel result - factor 4
- 12. Traditional approach ● Superpixel result - factor 9
- 13. Traditional approach ● Superpixel accuracy
- 14. Traditional approach ● Superpixel limitation ○ deterministic and strong assumption ( Similarity of neighboring pixels )
- 15. New approach ● Deep learning pixel-to-pixel segmentation. ○ Hand labelled mask is needed. ○ Let’s generate it ! From : Ra Gyoung Yoon et al, “Quantitative assesment of change in regional disease patterns on serial HRCT of fibrotic interstitial pneumonia with texture-based automated quantification system”. 2012.
- 16. Mask generation ● A naive approach → Failed. ○ Because the neural network have learned deterministic patterns instead of lung disease patterns. Honeycombing Emphysema
- 17. Mask generation ● Ken Perlin, “An image Synthesizer”, 1985 ○ natural appearing textures ○ gradient based fractal noise ○ heavily used in game business
- 18. Mask generation ● One random Perlin noise ( simplex noise ) ● two randomly selected ROI patches ConsolidationGGO Mask ROI Patch
- 19. Mask generation ● 547 patches → Infinite patches ( O(1006xN ) )
- 20. Model architecture ● UNet + SWWAE architecture ○ Olaf et al, “U-Net: Convolutional Networks for Biomedical Image Segmentation”, 2015 ○ Junbo et al, “Stacked What-Where Auto-encoders”, 2015
- 22. Deep learning approach ● pixel-to-pixel segmentation result
- 23. Deep learning approach ● pixel-to-pixel segmentation result
- 24. Deep learning approach ● pixel-to-pixel segmentation result
- 25. Deep learning approach ● pixel-to-pixel segmentation accuracy
- 26. High resolution segmentation ● 20 x 20 patches per 512 x 512 image ○ (512 - 20 + 1)2 → Too expensive
- 27. High resolution segmentation ● Fully convolutional layer used ○ Various sized image input available
- 28. High resolution segmentation ● 200 x 80 grids
- 29. High resolution segmentation ● 500 x 20 grid ( Vertical grids )
- 30. High resolution segmentation ● 20 x 500 grid ( Horizontal grids )
- 31. High resolution segmentation ● Computation complexity
- 32. High resolution segmentation ● Results ( Hortz )
- 33. High resolution segmentation ● Results ( Vert )
- 34. High resolution segmentation ● Results ( Mix )
- 35. High resolution segmentation ● Comparison - Accuracy
- 36. High resolution segmentation ● Comparison - computation time
- 37. Our contributions ● A simple and practical pixel mask generation method for DILD ROI dataset using Perlin noise. ○ No radiologist mask needed. ● We applied state-of-the-art deep CNN based pixel-to-pixel segmentation method to DILD dataset. ○ High accuracy with reasonable computing time.
- 38. Thank you !!!