ABSTRACT
In this work we introduce a differentiable version of the Compositional Pattern Producing Network, called the DPPN. Unlike a standard CPPN, the topology of a DPPN is evolved but the weights are learned. A Lamarckian algorithm, that combines evolution and learning, produces DPPNs to reconstruct an image. Our main result is that DPPNs can be evolved/trained to compress the weights of a denoising autoencoder from 157684 to roughly 200 parameters, while achieving a reconstruction accuracy comparable to a fully connected network with more than two orders of magnitude more parameters. The regularization ability of the DPPN allows it to rediscover (approximate) convolutional network architectures embedded within a fully connected architecture. Such convolutional architectures are the current state of the art for many computer vision applications, so it is satisfying that DPPNs are capable of discovering this structure rather than having to build it in by design. DPPNs exhibit better generalization when tested on the Omniglot dataset after being trained on MNIST, than directly encoded fully connected autoencoders. DPPNs are therefore a new framework for integrating learning and evolution.
- J. Bayer, D. Wierstra, J. Togelius, and J. Schmidhuber. Evolving memory cell structures for sequence learning. In Artificial Neural Networks--ICANN 2009, pages 755--764. Springer, 2009. Google ScholarDigital Library
- L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062, 2014.Google Scholar
- M. Denil, B. Shakibi, L. Dinh, N. de Freitas, et al. Predicting parameters in deep learning. In Advances in Neural Information Processing Systems, pages 2148--2156, 2013.Google ScholarDigital Library
- J. Duchi, E. Hazan, and Y. Singer. Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research, 12:2121--2159, 2011. Google ScholarDigital Library
- D. J. Felleman and D. C. Van Essen. Distributed hierarchical processing in the primate cerebral cortex. Cerebral cortex, 1(1):1--47, 1991.Google ScholarCross Ref
- K. Fukushima. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological cybernetics, 36(4):193--202, 1980.Google ScholarCross Ref
- A. Graves. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013.Google Scholar
- I. Harvey. The microbial genetic algorithm. In Advances in artificial life. Darwin Meets von Neumann, pages 126--133. Springer, 2011. Google ScholarDigital Library
- K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015.Google Scholar
- D. H. Hubel and T. N. Wiesel. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. The Journal of physiology, 160(1):106, 1962.Google ScholarCross Ref
- M. Jaderberg, A. Vedaldi, and A. Zisserman. Speeding up convolutional neural networks with low rank expansions. arXiv preprint arXiv:1405.3866, 2014.Google Scholar
- Ł. Kaiser and I. Sutskever. Neural gpus learn algorithms. arXiv preprint arXiv:1511.08228, 2015.Google Scholar
- D. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.Google Scholar
- A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097--1105, 2012.Google ScholarDigital Library
- B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. Human-level concept learning through probabilistic program induction. Science, 350(6266):1332--1338, 2015.Google ScholarCross Ref
- Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4):541--551, 1989. Google ScholarDigital Library
- Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278--2324, 1998.Google ScholarCross Ref
- V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529--533, 2015.Google ScholarCross Ref
- G. Morse, S. Risi, C. R. Snyder, and K. O. Stanley. Single-unit pattern generators for quadruped locomotion. In Proceedings of the 15th annual conference on Genetic and evolutionary computation, pages 719--726. ACM, 2013. Google ScholarDigital Library
- A. Nguyen, J. Yosinski, and J. Clune. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on, 2015.Google ScholarCross Ref
- J. K. Pugh and K. O. Stanley. Evolving multimodal controllers with hyperneat. In Proceedings of the 15th annual conference on Genetic and evolutionary computation, pages 735--742. ACM, 2013. Google ScholarDigital Library
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning representations by back-propagating errors. Cognitive modeling, 5:3, 1988.Google Scholar
- M. Santos, E. Szathmáry, and J. F. Fontanari. Phenotypic plasticity, the baldwin effect, and the speeding up of evolution: The computational roots of an illusion. Journal of theoretical biology, 371:127--136, 2015.Google Scholar
- J. Secretan, N. Beato, D. B. D'Ambrosio, A. Rodriguez, A. Campbell, J. T. Folsom-Kovarik, and K. O. Stanley. Picbreeder: A case study in collaborative evolutionary exploration of design space. Evolutionary Computation, 19(3):373--403, 2011. Google ScholarDigital Library
- K. O. Stanley. Compositional pattern producing networks: A novel abstraction of development. Genetic programming and evolvable machines, 8(2):131--162, 2007. Google ScholarDigital Library
- K. O. Stanley, D. B. D'Ambrosio, and J. Gauci. A hypercube-based encoding for evolving large-scale neural networks. Artificial life, 15(2):185--212, 2009. Google ScholarDigital Library
- K. O. Stanley and R. Miikkulainen. Evolving neural networks through augmenting topologies. Evolutionary computation, 10(2):99--127, 2002. Google ScholarDigital Library
- P. Verbancsics and J. Harguess. Generative neuroevolution for deep learning. arXiv preprint arXiv:1312.5355, 2013.Google Scholar
- P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, pages 1096--1103. ACM, 2008. Google ScholarDigital Library
- Z. Yang, M. Moczulski, M. Denil, N. de Freitas, A. Smola, L. Song, and Z. Wang. Deep fried convnets. In International Conference on Computer Vision (ICCV), 2015. Google ScholarDigital Library
- X. Yao, Y. Liu, and G. Lin. Evolutionary programming made faster. Evolutionary Computation, IEEE Transactions on, 3(2):82--102, 1999. Google ScholarDigital Library
- W. Zaremba. An empirical exploration of recurrent network architectures.Google Scholar
Index Terms
- Convolution by Evolution: Differentiable Pattern Producing Networks
Recommendations
Scaffolding for interactively evolving novel drum tracks for existing songs
Evo'08: Proceedings of the 2008 conference on Applications of evolutionary computingA major challenge in computer-generated music is to produce music that sounds natural. This paper introduces NEAT Drummer, which takes steps toward natural creativity. NEAT Drummer evolves a kind of artificial neural network called a Compositional ...
Convolutional adaptive denoising autoencoders for hierarchical feature extraction
Convolutional neural networks (CNNs) are typical structures for deep learning and are widely used in image recognition and classification. However, the random initialization strategy tends to become stuck at local plateaus or even diverge, which results ...
A hypercube-based encoding for evolving large-scale neural networks
Research in neuroevolution---that is, evolving artificial neural networks (ANNs) through evolutionary algorithms---is inspired by the evolution of biological brains, which can contain trillions of connections. Yet while neuroevolution has produced ...
Comments