Network Dissection: Quantifying Interpretability of Deep Visual Representations

David Bau*, Bolei Zhou*, Aditya Khosla, Aude Oliva, Antonio Torralba
Massachusetts Institute of Technology

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Selected units are shown from three state-of-the-art network architectures when trained to classify images of places (places-365). Many individual units respond to specific high-level concepts (object segmentations) that are not directly represented in the training set (scene classifications).

Why we study interpretable units

Interpretable units are interesting because they hint that deep networks may not be completely opaque black boxes.

However, the observations of interpretability up to now are just a hint: there is not yet a complete understanding of whether or how interpretable units are evidence of a so-called distentangled representation.

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AlexNet-Places205 conv5 unit 215: castles
AlexNet-Places205 conv5 unit 13: lamps
AlexNet-Places205 conv5 unit 53: stairways

What is Network Dissection?

Our paper investigates three questions:

  1. What is a disentangled representation, and how can its factors be quantified and detected?
  2. Do interpretable hidden units reflect a special alignment of feature space, or are interpretations a chimera?
  3. What conditions in state-of-the-art training lead to representations with greater or lesser entanglement?

Network Dissection is our method for quantifying interpretability of individual units in a deep CNN (i.e., our answer to question #1). It works by measuring the alignment between unit response and a set of concepts drawn from a broad and dense segmentation data set called Broden.

By measuring the concept that best matches each unit, Net Dissection can break down the types of concepts represented in a layer: here the 256 units of AlexNet conv5 trained on Places represent many objects and textures, as well as some scenes, parts, materials, and a color.

Are interpretations a chimera?

Network dissection shows that interpretable concepts are unusual orientations of representation space. Their emergence is evidence that the network is decomposing intermediate concepts, answering question #2.

Interpretability drops as the basis is gradually changed towards a random basis. Contradicting the prevailing wisdom, interpretability is not isotropic in representation space, and networks do appear to learn axis-aligned decompositions.

What affects interpretability?

This brings us to question #3: what conditions lead to higher or lower levels of interpretability?

Interpretability of ResNet > VGG > GoogLeNet > AlexNet, and in terms of primary training tasks, we find Places365 > Places205 > ImageNet.
Interpretability varies widely under a range of self-supervised tasks, and none approaches interpretability from supervision by ImageNet or Places.

We find that interpretabile units are found in representations of the major architectures for vision, and interpretable units also emerge under different training conditions including (to lesser degree) self-supervised tasks.

The code you find here will let you reproduce our interpretability benchmarks, and will allow you measure and find ways to improve interpretability in your own deep CNNs.


Network Dissection also allows us to understand how emergent concepts appear when training a model: in particular, it can quantify the change of representations under fine-tuning.

When learning from scratch, units change from detectors for low-level patterns or visually simple concepts such as "road" to detectors for more complex higher-level concepts such as "car".
When fine-tuning a model to solve a new problems, internal units can change roles. This "dog" detector becomse a "waterfall" detector when retrained from object to scene classification.

Network Dissection Results

Related Work

Several lines of research have shown ways to analyze internal representations of deep neural networks.

Generative Visualizations of Individual Units

A. Mahendran and A. Vedaldi. Understanding deep image representations by inverting them. Computer Vision and Pattern Recognition (CVPR), 2015.
Comment: Computing visualizations of internal representations as function inversions that generate results with natural-image statistics.

A. Nguyen, A. Dosovitskiy, J. Yosinski, T. Brox, J. Clune. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. Advances in Neural Information Processing Systems (NIPS), 2016.
Comment: Generating remarkably realistic image visualizations for units in representations using techniques from generative adversarial networks.

K. Simonyan, A. Vedaldi, and A. Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. International Conference on Learning Representations Workshop, 2014.
Comment: Applying gradient descent to generate visualizations of units in convolutional neural networks.

J. Springenberg, A. Dosovitskiy, T.Brox, M. Riedmiller. Striving for simplicity: The all convolutional net. International Conference on Learning Representations Workshop, 2015.
Comment: Defines guided backpropagation to visualize features of convolutional neural networks.

Salience-based Visualizations of Individual Units

M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. European Conference on Computer Vision (ECCV), 2014.
Comment: Introduces deconvolutional networks for generating visualizations, and shows how visualizations can motivate improvements in a network architecture.

B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba. Object Detectors Emerge in Deep Scene CNNs. International Conference on Learning Representations (ICLR), 2015. [PDF][Code]
Comment: In this work we used AMT workers to observe emergent interpretable object detectors inside the CNN trained for classifying scenes.

B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba. Learning Deep Features for Discriminative Localization. Computer Vision and Pattern Recognition (CVPR), 2016. [PDF] [Webpage][Code]
Comment: In this work we used class activation mapping to reveal salience maps for individual units.

Visualizing Representations as a Whole

L.V. Maaten, G. Hinton. Visualizing data using t-SNE. Journal of Machine Learning Research (JMLR), 2008. [PDF]
Comment: T-SNE is a powerful dimensionality reduction technique that can be used for visualizing a representation space.

J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, H. Lipson. Understanding neural networks through deep visualization. International Conference on Machine Learning Deep Learning Workshop, 2015.
Comment: Describes an interactive interface for exploring a representation by visualizaing all its units at once.

Quantifying Internal Representations

P. Agrawal, R. Girshick, and J. Malik. Analyzing the performance of multilayer neural networks for object recognition. European Conference on Computer Vision (ECCV), 2014.
Comment: Investigates the "grandmother neuron" hypothesis inside convolutional networks.

A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson. CNN features off-the-shelf: an astounding baseline for recognition. arXiv:1403.6382, 2014.
Comment: Quantifies the power of hidden representations by measuring their ability to be applied in solving problems different from the original training goal.

J. Yosinski, J. Clune, Y. Bengio, and H. Lipson. How transferable are features in deep neural networks? Advances in Neural Information Processing Systems (NIPS), 2014.
Comment: Quantifies hidden representations by measuring the transferability of features by training subsets of layers under varied conditions.


MIT researchers can now track AI's decisions back to single neurons. The ability to measure bias in neural networks could be critical in fields like healthcare, where bias inherent in an algorithm's training data could be carried into treatment, or in determining why self-driving cars make certain decisions on the road for a safer vehicle.

MIT CSAIL research offers a fully automated way to peer inside neural nets. Already, the research is providing interesting insight into how neural nets operate, for example showing that a network trained to add color to black and white images ends up concentrating a significant portion of its nodes to identifying textures in the pictures.

Peering into neural networks. Because their system could frequently identify labels that corresponded to the precise pixel clusters that provoked a strong response from a given node, it could characterize the node's behavior with great specificity.... One of the researchers' experiments could conceivably shed light on a vexed question in neuroscience.


Bibilographic information for this work:

D. Bau*, B. Zhou*, A. Khosla, A. Oliva, and A. Torralba. "Network Dissection: Quantifying Interpretability of Deep Visual Representations." Computer Vision and Pattern Recognition (CVPR), 2017. Oral. [PDF][Code]

(*first two authors contributed equally.)

  title={Network Dissection: Quantifying Interpretability of Deep Visual Representations},
  author={Bau, David and Zhou, Bolei and Khosla, Aditya and Oliva, Aude and Torralba, Antonio},
  booktitle={Computer Vision and Pattern Recognition},

Acknowledgement: This work was partly supported by the National Science Foundation under Grants No. 1524817 to A.T., and No. 1532591 to A.T. and A.O.; the Vannevar Bush Faculty Fellowship program sponsored by the Basic Research Office of the Assistant Secretary of Defense for Research and Engineering and funded by the Office of Naval Research through grant N00014-16-1-3116 to A.O.; the MIT Big Data Initiative at CSAIL, the Toyota Research Institute MIT CSAIL Joint Research Center, Google and Amazon Awards, and a hardware donation from NVIDIA Corporation. B.Z. is supported by a Facebook Fellowship.