Visual Interpretability for Deep Learning: a Survey (1802.00614v2)

Abstract: This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations. Although deep neural networks have exhibited superior performance in various tasks, the interpretability is always the Achilles' heel of deep neural networks. At present, deep neural networks obtain high discrimination power at the cost of low interpretability of their black-box representations. We believe that high model interpretability may help people to break several bottlenecks of deep learning, e.g., learning from very few annotations, learning via human-computer communications at the semantic level, and semantically debugging network representations. We focus on convolutional neural networks (CNNs), and we revisit the visualization of CNN representations, methods of diagnosing representations of pre-trained CNNs, approaches for disentangling pre-trained CNN representations, learning of CNNs with disentangled representations, and middle-to-end learning based on model interpretability. Finally, we discuss prospective trends in explainable artificial intelligence.

• The paper presents a comprehensive review of six research directions for enhancing CNN interpretability.
• It details methods for visualization, diagnosis, and disentanglement using gradient-based techniques and explanatory graphs.
• The survey underscores that integrating interpretability into deep models improves debugging, trust, and human-machine interaction.

Visual Interpretability for Deep Learning: A Survey

The paper "Visual Interpretability for Deep Learning: a Survey" by Quanshi Zhang and Song-Chun Zhu presents a comprehensive review of the landscape of interpretability for Convolutional Neural Networks (CNNs). The survey meticulously delineates various dimensions through which interpretability in deep learning can be achieved, focusing on CNNs. This essay aims to succinctly capture the essence of the paper, analyzing its contributions and implications for future research.

Key Discussion Areas

Zhang and Zhu's paper categorizes the quest for interpretability in CNNs into six distinct but interrelated research directions, each focusing on a specific aspect of network understanding and modification:

1. Visualization of CNN Representations: The visualization techniques, primarily driven by gradient-based methods and up-convolutional networks, aim to backtrack and expose the image patterns that activate specific neural units. This serves as a foundational step for enabling deeper insights into the intermediate layers of CNNs.
2. Diagnosis of CNN Representations: Beyond visualization, diagnostic methods analyze the feature space of CNNs to uncover semantic meanings, potential flaws, and biases in the learned representations. This includes examining adversarial vulnerabilities and refining representations to align better with human cognitive interpretations of image data.
3. Disentanglement of Representations: The paper explores converting the complex, intertwined patterns in CNN filters into clear, interpretable graphical models, notably explanatory graphs. This disentanglement aims to elucidate the semantic hierarchies encoded within CNN layers, providing a global view of the internal logic of the network.
4. Learning Interpretable Models: The research reviewed moves beyond understanding pre-trained networks to proposing methods for training networks whose intermediate representations are inherently interpretable. Models like interpretable CNNs, capsule networks, and InfoGAN fall under this category, aiming for disentangled, semantically meaningful feature extraction during end-to-end training.
5. Middle-to-End Learning: Leveraging interpretable representations for weakly-supervised middle-to-end learning signifies a paradigm shift in how models can be trained with sparse annotations. The objective is to create mechanisms where human interactions at the semantic level directly influence the model's learning process.

Noteworthy Results and Claims

• Visualization Efficacy: Techniques like feature inversion and gradient-based visualizations have shown tangible success in tracing and understanding the patterns that CNN filters capture. These methods provide direct visual evidence of what CNN filters "perceive," which is crucial for both debugging and model trustworthiness.
• Disentangled Graphs: The creation of explanatory graphs and decision trees represents a significant stride towards transparent models. By mapping individual filters to specific object parts and detailing their compositional relationships, these approaches allow for a more structured and human-understandable representation of CNN knowledge.
• Training Approaches: Methods to train interpretable models, such as the capsule network and interpretable CNNs, have demonstrated that it is feasible to incorporate interpretability directly into the learning process without compromising performance. For example, the capsule network's ability to dissect latent object features into discrete, identifiable components represents a meaningful advance.

Implications and Future Directions

The paper underscores several critical implications for both the foundational theory and practical applications of AI:

• Trust and Debugging: Interpretability is paramount for trust in AI systems. Understanding how CNNs make decisions enables better debugging and refinement, particularly important in high-stakes applications like medical image analysis and autonomous driving.
• Human-Machine Interaction: Enhanced interpretability fosters more effective human-machine interaction. For instance, middle-to-end learning methodologies that incorporate human feedback loops can significantly reduce the dependency on extensive labeled datasets. This makes AI more accessible and adaptable to new tasks with minimal supervision.

Looking forward, the survey suggests several avenues for continued research:

• Semantic Representation and Debugging: Further efforts in quantifying and improving the semantic alignment of CNN representations with human cognition could yield more dependable models.
• Unified Models: The concept of merging multiple task-specific CNNs into a universal network with a shared semantic representation remains an ambitious yet promising goal.
• Middle-to-End Learning and Interaction: The potential for interactive model training and weak supervision to drastically reduce the annotation burden should continue to be a focal point for research, aiming for real-world applicability in diverse domains.

In conclusion, Zhang and Zhu's survey meticulously catalogues the state of interpretability in CNNs, highlighting substantial achievements and mapping out future challenges. Their work consolidates various strands of research into a coherent narrative, providing a valuable reference point for advancing the understanding and development of interpretable AI systems.