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Semelhante a ARCHITECTURAL CONDITIONING FOR DISENTANGLEMENT OF OBJECT IDENTITY AND POSTURE INFORMATION
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ARCHITECTURAL CONDITIONING FOR DISENTANGLEMENT OF OBJECT IDENTITY AND POSTURE INFORMATION
- 1. ARCHITECTURAL CONDITIONING FOR DISENTANGLEMENT OF OBJECT IDENTITY AND POSTURE INFORMATION 저자 : Kazutoshi Sagi, Takahiro Toizumi & Yuzo Senda Data Science Research Laboratories NEC Corporation https://openreview.net/forum?id=HkaYjG6Lf 정리 : 김홍배
- 2. 일반적으로 다양한 pose에 대한 image를 획득하여 Networks을 Pose에 대하여 Invariance하게 Training High cost approach 3D Object Identification Problem
- 3. Equivariance Φ Image(X) Latent(Z) Z1 Z2 𝑇𝑔 2 𝑇𝑔 1 Φ Transformation X1 X2 Z2 = 𝑻 𝒈 𝟐 Z1 = 𝑻 𝒈 𝟐 Φ(X1) = Φ(𝑻 𝒈 𝟏 X1 ) : Invariance is special case of equivariance where 𝑇𝑔 2 is the identity. X2 = 𝑇𝑔 1 X1 Z2 = 𝑇𝑔 2 Z1 : 주어진 Image의 pose변환에 대하여 Latent space상에서 명확한 변환관계를 찾을 수 있다면 ? Z1 ≠ Z2 but keeps the relationship Mapping ft’n(Φ(·))
- 4. ROLLABLE LATENT SPACE 0 1 2 3 4 5 6 7 8 9 10 11 0 1 2 3 4 5 6 7 8 9 10 11 0 1 2 3 4 5 6 7 8 9 10 11 Image Latent vector Shift by circular permutation Angular rotation 본 연구에서 제시한 아이디어 3 4 5 6 7 8 9 10 11 0 1 2 𝑋θ 𝑖 𝑋θ 𝑗 𝑍θ 𝑖 𝑍θ 𝑗
- 5. ROLLABLE LATENT SPACE Image space에서의 pose 변경(Angular rotation)이 Latent vector의 Circular Permutation에 의한 Shift로 나타낼 수 있다면 ? 2 space의 Mapping 관계를 명확하게 알 수 있으며 Training하지않은 다른 pose에서의 latent vector를 유추할 수 있다 ! 여기서는 Auto-Encoder를 살짝 바꿔서 강제로 학습을 시킨다
- 6. ROLLABLE LATENT SPACE 𝑋θ 𝑖 𝑋θ 𝑗 𝑍θ 𝑖 𝑍θ 𝑗 여기서 Roll(Z, s)는 𝑍θ 𝑖 를 shift parameter s(각도 차) 만큼 Cyclic permutation 시킨 후 Decoder쪽의 입력 latent vector로 준다. Encoder쪽 입력에 𝑋θ 𝑖 를 Decoder 쪽 출력에는 회전한 𝑋θ 𝑗 를 준다.
- 7. ROLLABLE LATENT SPACE 𝑋θ 𝑖 𝑋θ 𝑗 𝑍θ 𝑖 𝑍θ 𝑗 Decoder의 출력이 𝑋θ 𝑗 와 근사하도록 Networks을 훈련시키면 됨.
- 8. Feature Augmentation by RLS Classifier의 훈련 시 Image level에서의 augmentation이 필요없이 주어 진 image, 𝑋𝑖의 latent vector, 𝑍𝑖를 랜덤하게 shift 시킴으로서 Feature level에서의 augmentation이 가능
- 9. EXPERIMENTAL RESULTS - The encoder and the decoder just consist of one hidden fully connected layer with ReLU activation for each. - The number of the latent space dimentions is given as 24, which corresponds to 2 dimensions in 12 viewing directions Exp. 1 : DISENTANGLING 2D IMAGE ROTATION Reconstructions of the test dataset. An input and reconstructions in given rotation angles generated by are presented from the left column of each row.
- 10. EXPERIMENTAL RESULTS Exp. 2 : DISENTANGLING 3D OBJECT ROTATION • 809 chair models are selected • The first 500 models are used as a training set and the remaining 309 models are used as a test set. • Each chair model is rendered from 31 azimuth angles and 2 elevation angles (20 and 30) • A deep convolutional encoder-decoder architecture are used. • The number of the latent space dimensions is given as 992, which corresponds to 32 dimensions in 31 viewing directions.
- 11. EXPERIMENTAL RESULTS Exp. 2 : DISENTANGLING 3D OBJECT ROTATION (a): A network architecture used in the experiment of 3D object rotation. (b): Reconstructions of the test dataset. An input and reconstructions in given rotation angles are shown from the left column of each row.