Interpreting Deep Visual Representations via Network Dissection.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Deep convolutional neural networks (CNNs) rely on learning hidden representations to capture data variations.
• Understanding these internal representations is crucial for interpreting CNN behavior and improving model transparency.

Purpose of the Study:

• To introduce Network Dissection, a novel method for interpreting CNNs by labeling individual hidden units.
• To quantify the interpretability of CNN representations by aligning hidden units with visual semantic concepts.
• To explore factors influencing network interpretability and demonstrate the use of interpreted units for explaining predictions.

Main Methods:

• Developed Network Dissection to assign interpretable labels (e.g., colors, objects, scenes) to CNN units.
• Evaluated the alignment between hidden units and visual semantic concepts.
• Applied the method to various network architectures and training tasks, examining factors like training iterations and network architecture.

Main Results:

• Demonstrated that deep representations are more interpretable than random bases.
• Revealed that interpreted units can provide explicit explanations for CNN predictions.
• Identified factors such as training iterations, regularization, and network depth/width that affect interpretability.

Conclusions:

• Network Dissection provides a transparent and interpretable view into the internal workings of CNNs.
• Interpretability is a key property of deep neural networks, offering insights into learned hierarchical structures.
• The method facilitates a deeper understanding of what CNNs learn and how they make predictions.