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보다 유연한 이미지 변환을 하려면?

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Image-to-Image Translation 모두콘 2019 발표: http://moducon.kr/%EB%B3%B4%EB%8B%A4-%EC%9C%A0%EC%97%B0%ED%95%9C-%EC%9D%B4%EB%AF%B8%EC%A7%80-%EB%B3%80%ED%99%98%EC%9D%84-%ED%95%98%EB%A0%A4%EB%A9%B4/

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보다 유연한 이미지 변환을 하려면? 보잉 한국 기술연구소 (BKETC) 모두의 연구소 Self Driving Lab 이 광 희 Image-to-Image Translation
About Me 이 광 희 ( Kwang Hee Lee) • BKETC(보잉한국기술연구소) / Senior AI Technologist • 모두의 연구소 Self Driving LAB lkwanghee@gmail.com +82-10-9990-9474 ◆ Work Experience • 前 AIRI(인공지능연구원) / Senior Researcher • 前 TOVIS / Senior Researcher • 前 Samsung Medison / Senior Research Engineer ◆ Research Interests • Image-to-Image Translation • Arbitrary Style Transfer • XAI(eXplainable AI) • Visual SLAM
오늘의 발표주제
유연한 이미지 변환에 대해..
많은 이미지 변환 기법들이 있지만.. Generative Adversarial Networks(GANs) Artistic/Photo Style Transfer Deep Image Analogy Variational Autoencoder (VAE) Image Warping / Morphing Texture Synthesis …
그 중에서도 GAN기반의 Image-to-Image Translation
What is Image Translation? Source Domain Target Domain Image Translator Selfie2Anime Input Image Translated Image
Taxonomy of Image Translation Image Translation Paired Unpaired Unimodal Multi-modal Unimodal Multi-modal Multi-domain Multi-mapping Pix2Pix BicycleGAN CycleGAN DiscoGAN UNIT MUNIT DRIT StarGAN DRIT++ DMIT SDIT StarGAN v2
Paired vs Unpaired
Paired (Supervised) Image Translation Image-to-Image Translation with Conditional Adversarial Networks Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros CVPR, 2017. Pix2Pix (CVPR2017)
Unpaired (Unsupervised) Image Translation Jun-Yan Zhu*, Taesung Park*, Phillip Isola, and Alexei A. Efros. "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks", in IEEE ICCV 2017. CycleGAN (ICCV2017)
Unpaired Image Translation Unpaired Unimodal Multi-modal Multi-domain Multi-mapping
Diversity & Scalability
Unimodal(one-to-one) Translation G1 G2 G3 Diversity Scalability Generator
Unimodal(one-to-one) Translation G1 G2 G3 G1 G2 G3 G1 G2 G3 Diversity Scalability Generator
Unimodal(one-to-one) Translation Jun-Yan Zhu*, Taesung Park*, Phillip Isola, and Alexei A. Efros. "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks", in IEEE ICCV 2017. CycleGAN (ICCV2017) DiscoGAN (ICML2017) UNIT (NIPS2017)
 Loss = Adversarial Loss + Cycle Consistency Loss  Cycle Consistency Loss Unimodal(one-to-one) Translation
Unimodal(one-to-one) Translation UNIT (NIPS2017) G1 G2 CycleGAN (ICCV2017) DiscoGAN (ICML2017) c𝑥2𝑥1 𝑐1 𝑐2 𝑥2𝑥1 -Shared Latent space Assumption-Cycle Consistency Loss
Multi-domain Translation G Diversity Scalability Generator
Multi-domain Translation G G Diversity Scalability Generator
Multi-domain Translation StarGAN (CVPR2018) StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation Yunjey Choi1,2, Minje Choi1,2, Munyoung Kim2,3, Jung-Woo Ha2, Sung Kim2,4, and Jaegul Choo1,2 IEEE CVPR, 2018 (Oral)
Multi-domain Translation StarGAN(CVPR2018) G 𝑥 𝑁𝑥2𝑥1 c … - Domain Label - Unified Structure
Multi-modal Translation G1 G2 G3 Diversity ScalabilityGenerator
Multi-modal Translation G1 G2 G3 Diversity Scalability G1 G2 G3 Generator
Multi-modal Translation MUNIT (ECCV2018) DRIT (ECCV2018) Lee, Hsin-Ying, et al. "Diverse image-to-image translation via disentangled representations." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
Multi-modal Translation MUNIT (ECCV2018) DRIT(ECCV2018) c 𝑥2𝑥1 G1 G2 𝑠1 𝑠2 -Partially Shared Latent Space -Feature Disentanglement (separate content and style feature)
Multi-mapping Translation G Diversity Scalability
Multi-mapping Translation G Diversity Scalability G
Multi-mapping Translation Lee, Hsin-Ying, et al. "DRIT++: Diverse Image-to-Image Translation via Disentangled Representations." arXiv preprint arXiv:1905.01270 (2019). DRIT++(arXiv2019)
Multi-mapping Translation DRIT++ (arXiv2019) DMIT (Neurips2019) SDIT (ICM2019) StarGAN v2 (arXiv2019) G 𝑥 𝑁𝑥2𝑥1 s c -Unified Style Encoder
Comparison Unsupervised I2I Translation Models CycleGAN UNIT(L) DiscoGAN U-GAT-IT(AT) MUNIT(PL, FD) DRIT(PL, FD) StarGAN(U, DL) SDIT(U, DL, AT) DMIT (U, L, FD) DRIT++(U, L, FD, DL) U: Unified Structure L: Shared Latent Space (하나의 Latent Space로 통합) DL: Domain Label PL: Partially Latent Space FD: Feature Disentanglement (Content, Style feature를 분리) AT: Attention
기존의 I2I 모델들의 한계?
그 전에 먼저, Image Translation 문제를 세분화 해봅시다.
Style Transfer Object Transfiguration with Geometry Change Object Transfiguration without Geometry Change Aligned Data Unaligned Data (In the Wild)
Style Transfer Object Transfiguration with Geometry Change Object Transfiguration without Geometry Change Aligned Data Unaligned Data (In the Wild) 가장 잘됨!
Style Transfer Object Transfiguration with Geometry Change Object Transfiguration without Geometry Change Aligned Data Unaligned Data (In the Wild) 가장 잘됨!
Style Transfer Object Transfiguration with Geometry Change Object Transfiguration without Geometry Change Aligned Data Unaligned Data (In the Wild) 가장 잘됨! 배경색이 변하긴 하지만, 잘 되는 편..
Style Transfer Object Transfiguration with Geometry Change Object Transfiguration without Geometry Change Aligned Data Unaligned Data (In the Wild) 가장 잘됨! 배경색이 변하긴 하지만, 잘 되는 편..
Style Transfer Object Transfiguration with Geometry Change Object Transfiguration without Geometry Change Aligned Data Unaligned Data (In the Wild) 가장 잘됨! 배경색이 변하긴 하지만, 잘 되는 편.. Aligned Data에서는 잘 되지만, Wild Image에서는 실패함.
Style Transfer Object Transfiguration with Geometry Change Object Transfiguration without Geometry Change Aligned Data Unaligned Data (In the Wild) 혼합된 형태의 문제도 있음.
Style Transfer Object Transfiguration with Geometry Change Object Transfiguration without Geometry Change Aligned Data Unaligned Data (In the Wild) 혼합된 형태의 문제도 있음.
Object Transfiguration에서 기존 I2I 모델들의 한계는?
한계점: Geometry Change의 실패
한계점: 배경의 컬러 변질 및 형태정보 손실
한계점: 원치 않는 대상의 변환
이러한 문제 해결을 위한 연구들..
Attention GAN Attention Chen, Xinyuan, et al. "Attention-GAN for object transfiguration in wild images." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
InstaGANMo, Sangwoo, Minsu Cho, and Jinwoo Shin. "InstaGAN: Instance-aware Image-to-Image Translation." arXiv preprint arXiv:1812.10889 (2018).
TransGaGaWu, Wayne, et al. "Transgaga: Geometry-aware unsupervised image-to-image translation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
여전히 Geometry Change는 쉽지 않은 문제!
무엇이 Geometry Change를 어렵게 만들까?
Aligned vs Wild (Unaligned) Aligned Aligned
Aligned vs Wild (Unaligned) Aligned Aligned Unaligned Unaligned
Intra-class Variation Low Intra Class Variation
Intra-class Variation Low Intra Class Variation High Intra Class Variation
Inter-class Variation Low Inter Class Variation
Inter-class Variation Low Inter Class Variation High Inter Class Variation
Model and Hyperparameter Setting https://github.com/HsinYingLee/DRIT
우리가 Geometry Change를 잘 하기 위해 고려한 것들..
무엇을 변화시킬지.. 얼마나 변화시킬지.. 하나의 고정된 모델과 하이퍼파라미터..
이 세가지를 동시에 해결한 논문: U-GAT-IT
U-GAT-IT: Unsupervised Generative ATtentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation Junho Kim, MinJae Kim, Hyeonwoo Kang, Kwang Hee Lee https://github.com/taki0112/UGATIT https://arxiv.org/abs/1907.10830
이 세가지를 동시에 해결한 논문: U-GAT-IT
Summary - Propose a novel I2I model with the Attention module and the AdaLIN. - Attention module: distinguish between source and target domains - Guide the translation focusing on more important regions - AdaLIN: flexible control the amount of change in shape and texture without tuning (model architecture or hyperparameters) - Learnable normalization function
Background: Class Activation Mapping(CAM) Zhou, Bolei, et al. "Learning deep features for discriminative localization." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
Background : Normalization Function Normalization Whitening
Background : Normalization Function - Instance Norm(IN), Batch Norm(BN), Layer Norm(LN) - Adaptive Instance Norm(AdaIN), Conditional Instance Norm(CIN),… https://mlexplained.com/2018/11/30/an-overview-of-normalization-methods-in-deep- learning/?fbclid=IwAR3eC6oIW10_2Lwrvu55iOHxkKTLn0Q4e3jhPrtsOrIE4O-f4Fx6egUyTQI
Background : BIN(Batch-Instance Normalization) Nam, Hyeonseob, and Hyo-Eun Kim. "Batch-instance normalization for adaptively style- invariant neural networks." Advances in Neural Information Processing Systems. 2018.
Background : BIN(Batch-Instance Normalization) Nam, Hyeonseob, and Hyo-Eun Kim. "Batch-instance normalization for adaptively style- invariant neural networks." Advances in Neural Information Processing Systems. 2018. BN과 IN을 잘 결합하면 중요한 style은 유지하고 불필요한 style은 제거하는 효과를 가져옴.
Motivation : IN & LN Instance Normalization Layer Normalization ▪ 주로 Image generation/style transfer에서 좋은 성능을 냄. ▪ Channel–wise statistics를 고려함. ▪ 상대적으로 source domain content의 structure를 잘 유지함. ▪ Global statistics를 고려함. ▪ 상대적으로 source domain content의 structure를 덜 유지하고, target domain의 style을 더 잘 반영함.
Motivation : IN & LN Instance Normalization Layer Normalization ▪ 주로 Image generation/style transfer에서 좋은 성능을 냄. ▪ Channel–wise statistics를 고려함. ▪ 상대적으로 source domain content의 structure를 잘 유지함. ▪ Global statistics를 고려함. ▪ 상대적으로 source domain content의 structure를 덜 유지하고, target domain의 style을 더 잘 반영함. BIN처럼 잘 합치면, task에 따라서 적절히 normalization을 수행 할 수 있지 않을까?
Adaptive Layer-Instance Normalization (AdaLIN) Learned Parameter -Attention 모델이 Shape 변화의 양을 유연하게 조절하도록 가이드 -Normalization의 정도를 선택적으로 결정
Model Architecture
Model Architecture
Experimental Results
CAM Ablation Test
Attention Maps
Normalization Functions
U-GAT-IT U-GAT-ITInput InputCycleGAN UNIT MUNIT DRIT AGGAN CycleGAN UNIT MUNIT DRIT AGGAN
Conclusion and Future Work - Attention module 과 새로운 learnable normalization 기법인 AdaLIN 제안. - CAM기반의 attention module은 두 도메인간의 차이가 큰 영역을 더 잘 바꾸도록 가이드해 준다. - AdaLIN을 통해서 shape과 style 변환의 양을 model이나 hyper-parameter의 변경 없이 조절할 수 있었다. - Future work: 더 wild한 이미지 간의 변환, multi-mapping image translation
Thank You! Q&A

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