Comparing Deep Neural Nets with UMAP Tour
Neural networks should be interpretable to humans. In particular, there is a growing interest in concepts learned in a layer and similarity between layers. In this work, a tool, UMAP Tour, is built to visually inspect and compare internal behavior of real-world neural network models using well-aligned, instance-level representations. The method used in the visualization also implies a new similarity measure between neural network layers. Using the visual tool and the similarity measure, we find concepts learned in state-of-the-art models and dissimilarities between them, such as GoogLeNet and ResNet.
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Methods
1x1 Convolution •
Auxiliary Classifier •
Average Pooling •
Batch Normalization •
Bottleneck Residual Block •
Convolution •
Dense Connections •
Dropout •
Global Average Pooling •
Inception Module •
Kaiming Initialization •
Local Response Normalization •
Max Pooling •
ReLU •
Residual Block •
Residual Connection •
ResNet •
Softmax
