Graph-Based Similarity of Neural Network Representations
arXiv:2111.11165
Abstract
Understanding the black-box representations in Deep Neural Networks (DNN) is an essential problem in deep learning. In this work, we propose Graph-Based Similarity (GBS) to measure the similarity of layer features. Contrary to previous works that compute the similarity directly on the feature maps, GBS measures the correlation based on the graph constructed with hidden layer outputs. By treating each input sample as a node and the corresponding layer output similarity as edges, we construct the graph of DNN representations for each layer. The similarity between graphs of layers identifies the correspondences between representations of models trained in different datasets and initializations. We demonstrate and prove the invariance property of GBS, including invariance to orthogonal transformation and invariance to isotropic scaling, and compare GBS with CKA. GBS shows state-of-the-art performance in reflecting the similarity and provides insights on explaining the adversarial sample behavior on the hidden layer space.
References in corpus (5)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Similarity of Neural Network Representations Revisited
- Algorithms for Learning Kernels Based on Centered Alignment
- Interpreting and Improving Adversarial Robustness of Deep Neural Networks with Neuron Sensitivity
- Graph Structure of Neural Networks