deep visual learning on hypersphere - nvidia

Deep Visual Learning on HypersphereWeiyang Liu*, Zhen Liu*College of ComputingGeorgia Institute of Technology

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• Why Learning on Hypersphere

• Loss Design - Large-Margin Learning on Hypersphere

• Convolution Operator - Deep Hyperspherical Learning and Decoupled Networks

• Weight Regularization - Minimum Hyperspherical Energy for Regularizing Neural Networks

• Conclusion

Outline

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• Conclusion

Outline

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Why Learning on Hypersphere

• An empirical observation• Setting the output feature dimension as 2 in CNN• Directly visualizing the features without using T-SNE

Deep features are naturally distributed over a sphere! 4


• Euclidean distance is not suitable for high-dimensional data

More specifically,

In high-dimensional space, vectors tend to be orthogonal to each other, then this reduces to

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• Learning features on Hypersphere can well regularize the feature space.

In deep metric learning, features have to be normalized before entering the loss function.

Schroff et al. FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR 2015

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• Conclusion

Outline

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Large-Margin Learning on Hypersphere

• Standard CNN usually uses the softmax loss as the learning objective.

How to incorporate margin on hypersphere?

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• The intuition (from binary classification)If x belongs to class 1, original Softmax requires:

We want to make the classification more rigorous in order to produce a decision margin:

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Original Softmax Loss Large-Margin Softmax LossImposing large

margin

Normalizing classifier weights

Angular Softmax Loss

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Learned Feature Visualization

• 2D Feature Visualization on MNIST

• 3D Feature Visualization on CASIA Face Dataset

m=1 m=2 m=3 m=4

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Experimental Results• Face Verification

LFW and YTF dataset

SphereFace uses the angular large-margin softmax loss, achieving the state-of-the-art performance with only 0.5M training data.

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Experimental Results• Million-scale Face Recognition Challenge

MegaFace Challenge

SphereFace ranked No.1 from 2016.12 to 2017.4, and the current No. 1 entry is also developed based on SphereFace.

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Demo

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• Conclusion

Outline

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SphereNet

• Traditional Convolution

• HyperSpherical Convolution (SphereConv)

SphereConv normalizes each local patch of a feature map and each weight vector.

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SphereNet - Intuition from Fourier Transform

• Semantic information is mostly preserved with corrupted magnitude but not corrupted phase (angular information)

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Observation: The final feature is naturally decoupled, where the magnitude represents the intra-class variation.

Decoupled Convolution

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General Framework - Decoupled Convolution

• Decoupling angle and magnitude of feature vectors

• Allowing different designs of convolution operators for different tasks

Decoupled Convolution

Magnitude(intra-class variation)

Angle(semantic difference)

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• SphereConv

• BallConv

• TanhConv

• LinearConv

Example Choices of Magnitude

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• Linear

• Cosine

• Squared Cosine

Example Choices of Angle

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With SphereConv, the top-1 accuracy of CNNs on ImageNet can be improved by ~1%.

Generalization

Plain-CNN-9 Plain-CNN-12 ResNet-27

Baseline 58.31 61.42 65.54

SphereNet 59.23 62.27 66.49

* Different from the original NeurIPS paper:1) In ResNet, we use fully connected layer instead of average pooling to

obtain the final feature. We found it to be crucial for SphereNet.2) We add L2 decay, which slows down the optimization but results in

better performance.

Top-1 Accuracy (center crop) of baseline and SphereNet on ImageNet.

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•

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Adversarial Robustness and Optimization

* Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Mądry. 23

• Without BatchNorm, decoupled convolutions outperform the baseline.

• The bounded TanhConv can be optimized while unbounded ones fail.

Optimization Without BatchNorm

Accuracies of different convolution operators on Plain-CNN-9 without BatchNorm. N/C indicates ‘not converged’.

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Bounded convolution operators have better robustness against both fast gradient sign method (FGSM) attack and the multi-step version of FGSM.

Adversarial Robustness

Naturally Training

Adversarial Training

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It requires larger norm to attack decoupled convolution with bounded magnitude.

Adversarial Robustness

L2 and L_inf norms needed to attack models on samples in the test set. 26





• Conclusion

Outline

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Minimum Hyperspherical Energy

Intuition:

Better generalization More diversity of neurons Less redundancy

Paper [1] shows that, in one-hidden-layer network, maximizing diversity can eliminate spurious local minima.

If two weight vectors in one layer are close to each other, there is probably more redundancy.

28[1] Bo Xie, Yingyu Liang, and Le Song. Diverse neural network learns true target functions. arXiv preprint arXiv:1611.03131, 2016.


Proposed regularization: add repulsion forces between any pair of weight vectors (in one layer)

It connects to Thomson problem - to find a minimal configuration of electrons of an atom.

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Loss function:

This optimization problem is generally non-trivial. With s = 2, the problem is actually NP-hard.

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Although orthonormal loss seems similar, it does not yield ideal configuration of weights even in 3D case.


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MHE Loss is compatible with weight decay:

- MHE regularizes the angles of weights

- Weight decay regularizes the magnitude of weights

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Co-linearity Issue:

In this toy example, optimizing the original MHE results in colinear weight vectors

Half-space MHE:

Optimizing on pairwise angles between lines (instead of vectors).

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MHE - Ablation Study

MHE on 9 layer Plain CNN on CIFAR-10/100 dataset.

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• MHE consistently improve the performance of networks.

• In cases that the network is hard to optimize due to redundancy of neurons (small width/large depth), MHE helps more.


MHE with different depths of network on CIFAR-100.

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• MHE consistently improve the performance of networks.

• In cases that the network is hard to optimize due to redundancy of neurons (small width/large depth), MHE helps more.


MHE with different widths of network on CIFAR-100.

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MHE can improve performance of networks on ImageNet.

MHE Application - Image Recognition

Top-1 error (center crop) of models on ImageNet.

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We add MHE loss to the angular softmax loss in SphereFace. We call the resulted model SphreFace+.

Synergy:• Angular softmax loss - intra-class compactness • MHE loss - inter-class separability.

MHE Application - Face Recognition

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MHE Application - Face Recognition

Comparison to State-of-the-art results.

Comparison between SphereFace and SphereFace+.

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Applying MHE to the final classifier enforces the prior that all categories have the same importance and thus improves performance.

MHE Application - Class Imbalanced Recognition

Results on class imbalanced recognition on CIFAR-10.

* Single - Reduce the number of samples in only one category by 90%. Multiple - Reduce the number of samples in multiple categories with different weights. Details are shown in the paper.

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MHE Application - Class Imbalanced Recognition

The category with less data tends to be ignored

Visualization for the final CNN feature.41

With MHE added to the discriminator, the inception score of spectral GAN can be improved from 7.42 to 7.68.

MHE Application - GAN

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• Conclusion

Outline

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• We introduce a hyperspherical learning framework for deep visual learning, where all the neurons and classifiers are learned over a hypersphere.

• Large-margin learning on hypersphere is very beneficial to tasks like biometric verification and person re-id where features are expected to have large inter-class variation.

• Hyperspherical networks and decoupled networks are natural generalization of applying the hyperspherical learning to every layer of the network.

• Minimum hyperspherical energy is a generic regularization that aims to diversify the neurons on a hypersphere can improve the generalization.

Conclusion

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SphereFace: https://github.com/wy1iu/sphereface

SphereNet: https://github.com/wy1iu/SphereNet

DCNet: https://github.com/wy1iu/DCNets

MHE: https://github.com/wy1iu/MHE

SphereFace+: https://github.com/wy1iu/sphereface-plus

Source Code

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Architecture for SphereNet on ImageNet experiment

Plain-CNN-9: 7x7 conv - maxpool - 3*(3x3 conv, 64) - 3*(3x3 conv, 128) - 3*(3x3 conv, 256) - fc(512) - classifier

Plain-CNN-12: 7x7 conv - maxpool - 3*(3x3 conv, 64) - 3*(3x3 conv, 128) - 3*(3x3 conv, 256) - 3*(3x3 conv, 512) - fc(512) - classifier

ResNet-27: 7x7 conv - maxpool - 3*(3x3 ResBlock, 64) - 3*(3x3 ResBlock, 128) - 3*(3x3 ResBlock, 256) - 3*(3x3 ResBlock, 512) - fc(512) - classifier

Appendix

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Architecture for MHE ablation study on CIFAR-10/100

Appendix

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deep visual learning on hypersphere - nvidia

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