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Style space analysis paper review !

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taeseon ryu

안녕하세요 딥러닝 논문읽기 모임입니다 오늘 업로드된 논문 리뷰 영상은 2021 CVPR에서 oral Accept된 style space analysis 라는 제목의 논문 입니다. 논문은 다양한 데이터 세트에서 pretrained model을 사용하여 이미지 생성을 위한 최첨단 아키텍처인 StyleGAN2의 latent space를 탐색하고 분석하는 논문입니다. 먼저 채널별 스타일 매개변수의 공간인 StyleSpace가 이전 작업에서 탐색한 다른 중간 잠재 공간보다 훨씬 더 얽혀 있음을 보여줍니다. 각각이 고도로 지역화되고 얽힌 방식으로 고유한 시각적 속성을 제어하는 ​​것으로 표시되는 스타일 채널의 대규모 컬렉션을 검색하는 방법을 설명합니다. Style Gan1, 2를 한번 리뷰 해보신 분들이라면 더 재밌게 리뷰가 가능하실것 같습니다. 오늘도 많은 관심 미리 감사드립니다!

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StyleSpace Analysis (CVPR 2021 oral) 김상현, 고형권, 허다운, 조경진, 전선영, 김준철 발표: 고형권
Background: StyleGAN, StyleGAN2
Background: Latent Manipulation • W space à 512 dim • W+ space à 18 * 512 = 9216 dim 512 18*512 512 512 512 512 512 512
Mod Demod A 𝑊 𝑆! 3x3 Conv + B 𝑏! Upsample Upsample Mod Demod A 𝑊 𝑆" 3x3 Conv + B 𝑏" 𝑤" + A 𝑊 𝑆#$% Mod toRGB 𝑤! 𝑍 FC Normalize FC FC FC FC FC FC FC 𝑊 Key idea: StyleSpace (S space)
W space à 512 dim W+ space à 9216 dim S space à 9088 dim
Contributions: Disentangled controls • Evaluation using DCI metrics • D (Disentanglement) • C (Completeness) • I (Informativeness) Eastwood, C., & Williams, C. K. (2018, February). A framework for the quantitative evaluation of disentangled representations. In International Conference on Learning Representations.
https://www.youtube.com/watch?v=HsTwVqlJPmE
Sample Results
https://github.com/hyungkwonko/StyleSpace-pytorch
• Any questions?
3. Disentanglement of StyleGAN latent spaces • StyleGAN2 trained on FFHQ • 40 binary classifiers pretrained on CelebA attributes • Used only 31 attributes 𝑍 𝐶 = [smile=1, 0, 1, 1, 1, …, 0] len(C) = 31 StyleGAN2 classifier 500K generated images
3. Disentanglement of StyleGAN latent spaces (Using inversion) (Using random sampling)
4. Detecting locally-active style channels • Find locally-active style channels using gradient map • Backprop to compute gradient map • Overlap btwn. semantic region and gradient map
4. Detecting locally-active style channels https://www.youtube.com/watch?v=HsTwVqlJPmE How much overlap???
5. Detecting attribute-specific channels • the differences between the mean style vectors • the positive examples (exemplar mean) • the entire generated distribution (population mean) Normalized style vector u = style channel 9088 dim
5. Detecting attribute-specific channels • Advantage: Requires only 10-30 positive examples • Advantage2: do not require negative samples (4) Mouth à (5) Smile (5) Smile
6. Disentangled attribute manipulation • Comparison to previous works • Attribute Dependency (AD) • Quantify independent manipulation • Change of logits C=[smile=0, 1, …] C=[smile=1, 1, …] Logit=[smile=0.02, 0.78, …] Logit=[smile=0.98, 0.83, …]
6. Disentangled attribute manipulation
7. Real-image editing (using In-domain GAN)
Less ID change (supp) • ID changes less than baselines (e.g., GANSpace, InterfaceGAN) • Euclidean distance between Features when embedded using FaceNet
• Any questions?

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