Abstract
The practice of transforming raw data to a feature space so that inference can be performed in that space has been popular for many years. Recently, rapid progress in deep neural networks has given both researchers and practitioners enhanced methods that increase the richness of feature representations, be it from images, text or speech. In this work we show how a constructed latent space can be explored in a controlled manner and argue that this complements well founded inference methods. For constructing the latent space a Variational Autoencoder is used. We present a novel controller module that allows for smooth traversal in the latent space and construct an end-to-end trainable framework. We explore the applicability of our method for performing spatial transformations as well as kinematics for predicting future latent vectors of a video sequence.
This work was supported by the Australian Research Council Centre of Excellence for Robotic Vision (project number CE1401000016).
Y. Zuo and G. Avraham are contributed equally.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Utgoff, P.E., Stracuzzi, D.J.: Many-layered learning. Neural Comput. 8, 2497–2529 (2002)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer Vision and Pattern Recognition (2014)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional models for semantic segmentation. In: Computer Vision and Pattern Recognition (2015)
Lotter, W., Kreiman, G., Cox, D.: Deep predictive coding networks for video prediction and unsupervised learning. arXiv preprint arXiv:1605.08104 (2016)
Mathieu, M., Couprie, C., LeCun, Y.: Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440 (2015)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Liang, X., Lee, L., Dai, W., Xing, E.P.: Dual motion GAN for future-flow embedded video prediction. arXiv preprint (2017)
Yoo, Y., Yun, S., Chang, H.J., Demiris, Y., Choi, J.Y.: Variational autoencoded regression: high dimensional regression of visual data on complex manifold. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3674–3683 (2017)
Kulkarni, T.D., Whitney, W.F., Kohli, P., Tenenbaum, J.: Deep convolutional inverse graphics network. In: Advances in Neural Information Processing Systems, pp. 2539–2547 (2015)
Santana, E., Hotz, G.: Learning a driving simulator. arXiv preprint arXiv:1608.01230 (2016)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: Infogan: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems, 2172–2180 (2016)
Larsen, A.B.L., Sønderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300 (2015)
Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 161–168. ACM (2006)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 832–844 (1998)
Breiman, L.: Bagging predictors. Mach. Learn. 24, 123–140 (1996)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63, 3–42 (2006)
Ozuysal, M., Calonder, M., Lepetit, V., Fua, P.: Fast keypoint recognition using random ferns. IEEE Trans. Pattern Anal. Mach. Intell. 32, 448–461 (2010)
Kursa, M.B.: rFerns: an implementation of the random ferns method for general-purpose machine learning. arXiv preprint arXiv:1202.1121 (2012)
Bulo, S.R., Kontschieder, P.: Neural decision forests for semantic image labelling. In: CVPR, vol. 5, pp. 81–88 (2014)
Kontschieder, P., Fiterau, M., Criminisi, A., Bulo, S.R.: Deep neural decision forests. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1467–1475. IEEE (2015)
Zuo, Y., Drummond, T.: Fast residual forests: rapid ensemble learning for semantic segmentation. In: Conference on Robot Learning, pp. 27–36 (2017)
Zuo, Y., Avraham, G., Drummond, T.: Generative adversarial forests for better conditioned adversarial learning. arXiv preprint arXiv:1805.05185 (2018)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2013)
Rosca, M., Lakshminarayanan, B., Warde-Farley, D., Mohamed, S.: Variational approaches for auto-encoding generative adversarial networks. arXiv preprint arXiv:1706.04987 (2017)
Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings CVPR 1991, pp. 586–591. IEEE (1991)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017)
Dumoulin, V., et al.: Adversarially learned inference. arXiv preprint arXiv:1606.00704 (2016)
Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial feature learning. arXiv preprint arXiv:1605.09782 (2016)
Mescheder, L., Nowozin, S., Geiger, A.: Adversarial variational bayes: unifying variational autoencoders and generative adversarial networks. arXiv preprint arXiv:1701.04722 (2017)
Kingma, D.P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., Welling, M.: Improved variational inference with inverse autoregressive flow. In: Advances in Neural Information Processing Systems, pp. 4743–4751 (2016)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)
LeCun, Y.: The MNIST database of handwritten digits. http://yann.lecun.com/exdb/mnist/
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the kitti dataset. Int. J. Robot. Res. 32, 1231–1237 (2013)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zuo, Y., Avraham, G., Drummond, T. (2019). Traversing Latent Space Using Decision Ferns. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11361. Springer, Cham. https://doi.org/10.1007/978-3-030-20887-5_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-20887-5_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20886-8
Online ISBN: 978-3-030-20887-5
eBook Packages: Computer ScienceComputer Science (R0)