Network Dissection: Quantifying Interpretability of Deep Visual Representations
This work addresses the need for interpretability in deep learning for computer vision researchers, though it is incremental as it builds on existing methods for semantic analysis.
The authors tackled the problem of quantifying interpretability in deep visual representations by proposing Network Dissection, a framework that evaluates alignment between CNN hidden units and semantic concepts, and applied it to analyze various network architectures and training methods, revealing insights beyond discriminative power.
We propose a general framework called Network Dissection for quantifying the interpretability of latent representations of CNNs by evaluating the alignment between individual hidden units and a set of semantic concepts. Given any CNN model, the proposed method draws on a broad data set of visual concepts to score the semantics of hidden units at each intermediate convolutional layer. The units with semantics are given labels across a range of objects, parts, scenes, textures, materials, and colors. We use the proposed method to test the hypothesis that interpretability of units is equivalent to random linear combinations of units, then we apply our method to compare the latent representations of various networks when trained to solve different supervised and self-supervised training tasks. We further analyze the effect of training iterations, compare networks trained with different initializations, examine the impact of network depth and width, and measure the effect of dropout and batch normalization on the interpretability of deep visual representations. We demonstrate that the proposed method can shed light on characteristics of CNN models and training methods that go beyond measurements of their discriminative power.