Dilated DenseNets for Relational Reasoning

Despite their impressive performance in many tasks, deep neural networks often struggle at relational reasoning. This has recently been remedied with the introduction of a plug-in relational module that considers relations between pairs of objects. Unfortunately, this is combinatorially expensive. In this extended abstract, we show that a DenseNet incorporating dilated convolutions excels at relational reasoning on the Sort-of-CLEVR dataset, allowing us to forgo this relational module and its associated expense.

[1]  Luis Miguel Bergasa,et al.  Efficient ConvNet for real-time semantic segmentation , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[2]  Li Fei-Fei,et al.  CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Jason Weston,et al.  Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks , 2015, ICLR.

[4]  Razvan Pascanu,et al.  A simple neural network module for relational reasoning , 2017, NIPS.

[5]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[6]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[7]  Heiga Zen,et al.  WaveNet: A Generative Model for Raw Audio , 2016, SSW.

[8]  Amos J. Storkey,et al.  Moonshine: Distilling with Cheap Convolutions , 2017, NeurIPS.

[9]  N. Pierce.  Origin of Species , 1914, Nature.

[10]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[11]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Eduardo Romera,et al.  ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation , 2018, IEEE Transactions on Intelligent Transportation Systems.