On-Off Pattern Encoding and Path-Count Encoding as Deep Neural Network Representations (2401.09518v1)
Abstract: Understanding the encoded representation of Deep Neural Networks (DNNs) has been a fundamental yet challenging objective. In this work, we focus on two possible directions for analyzing representations of DNNs by studying simple image classification tasks. Specifically, we consider \textit{On-Off pattern} and \textit{PathCount} for investigating how information is stored in deep representations. On-off pattern of a neuron is decided as on' oroff' depending on whether the neuron's activation after ReLU is non-zero or zero. PathCount is the number of paths that transmit non-zero energy from the input to a neuron. We investigate how neurons in the network encodes information by replacing each layer's activation with On-Off pattern or PathCount and evaluating its effect on classification performance. We also examine correlation between representation and PathCount. Finally, we show a possible way to improve an existing DNN interpretation method, Class Activation Map (CAM), by directly utilizing On-Off or PathCount.
- Hervé Abdi. The kendall rank correlation coefficient. Encyclopedia of Measurement and Statistics. Sage, Thousand Oaks, CA, pages 508–510, 2007.
- Towards better understanding of gradient-based attribution methods for deep neural networks. arXiv preprint arXiv:1711.06104, 2017.
- Improving deep neural networks for lvcsr using rectified linear units and dropout. In 2013 IEEE international conference on acoustics, speech and signal processing, pages 8609–8613. IEEE, 2013.
- Visualizing higher-layer features of a deep network. University of Montreal, 1341(3):1, 2009.
- Complexity of linear regions in deep networks. arXiv preprint arXiv:1901.09021, 2019.
- Deep relu networks have surprisingly few activation patterns. In Advances in Neural Information Processing Systems, pages 361–370, 2019.
- Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
- Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017.
- Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.
- Does resnet learn good general purpose features? In Proceedings of the 2017 International Conference on Artificial Intelligence, Automation and Control Technologies, pages 1–5, 2017.
- Inception and resnet features are (almost) equivalent. Cognitive Systems Research, 59:312–318, 2020.
- On the number of linear regions of deep neural networks. In Advances in neural information processing systems, pages 2924–2932, 2014.
- Rectified linear units improve restricted boltzmann machines. In ICML, 2010.
- Activation functions: Comparison of trends in practice and research for deep learning. arXiv preprint arXiv:1811.03378, 2018.
- On the number of response regions of deep feed forward networks with piece-wise linear activations. arXiv preprint arXiv:1312.6098, 2013.
- Adversarial defense through network profiling based path extraction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4777–4786, 2019.
- Searching for activation functions. arXiv preprint arXiv:1710.05941, 2017.
- Evaluating the visualization of what a deep neural network has learned. IEEE transactions on neural networks and learning systems, 28(11):2660–2673, 2016.
- Restricting the flow: Information bottlenecks for attribution. arXiv preprint arXiv:2001.00396, 2020.
- Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pages 618–626, 2017.
- Sagar Sharma. Activation functions in neural networks. Towards Data Science, 6, 2017.
- Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
- Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9, 2015.
- Interpret neural networks by identifying critical data routing paths. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8906–8914, 2018.
- Visualizing and comparing convolutional neural networks. arXiv preprint arXiv:1412.6631, 2014.
- Visualizing and understanding convolutional networks. In European conference on computer vision, pages 818–833. Springer, 2014.
- On rectified linear units for speech processing. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 3517–3521. IEEE, 2013.
- Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2921–2929, 2016.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
