Learning Bottleneck Concepts in Image Classification (2304.10131v1)

Abstract: Interpreting and explaining the behavior of deep neural networks is critical for many tasks. Explainable AI provides a way to address this challenge, mostly by providing per-pixel relevance to the decision. Yet, interpreting such explanations may require expert knowledge. Some recent attempts toward interpretability adopt a concept-based framework, giving a higher-level relationship between some concepts and model decisions. This paper proposes Bottleneck Concept Learner (BotCL), which represents an image solely by the presence/absence of concepts learned through training over the target task without explicit supervision over the concepts. It uses self-supervision and tailored regularizers so that learned concepts can be human-understandable. Using some image classification tasks as our testbed, we demonstrate BotCL's potential to rebuild neural networks for better interpretability. Code is available at https://github.com/wbw520/BotCL and a simple demo is available at https://botcl.liangzhili.com/.

• The paper introduces BotCL, a novel approach leveraging self-supervised learning to extract and localize human-interpretable image concepts.
• It employs slot attention and tailored regularizers to learn distinctive, binary concept activations without requiring predefined labels.
• Experimental evaluations on diverse datasets show that BotCL achieves competitive accuracy while significantly improving model transparency.

Learning Bottleneck Concepts in Image Classification

The paper entitled "Learning Bottleneck Concepts in Image Classification" introduces the Bottleneck Concept Learner (BotCL), a novel approach aimed at enhancing the interpretability of image classification processes by leveraging concept-based frameworks without the need for explicit supervision of the concepts themselves. This paper provides a comprehensive examination of BotCL's mechanisms, from its architectural design to training methodologies, demonstrating its potential in improving both the comprehension and transparency of deep neural network (DNN) decision-making.

Overview of BotCL

The fundamental premise of BotCL is to utilize the presence or absence of learned concepts as the core representation of images, which are then used for classification. By employing self-supervised learning and tailored regularizers, BotCL ensures that these concepts remain human-understandable. The system does not rely on predefined concept labels, making it versatile and reducing human intervention in concept selection, which often demands significant annotation efforts and may not align with how DNNs perceive data.

Technical Methodology

1. Concept Extraction and Self-Supervision: BotCL employs a slot attention-based mechanism to identify significant concepts within an image by comparing parts of the feature map against learned concept prototypes. This mechanism not only identifies but also localizes where a concept manifests in an image.
2. Self-supervision Mechanisms: Two approaches are proposed—contrastive loss, inspired by recent advancements in self-supervised learning, particularly for natural image tasks, and reconstruction loss, more applicable when visual elements are consistently correlated with specific spatial positions.
3. Regularization: BotCL incorporates individual consistency and mutual distinctiveness losses to promote coherent and uniquely identifiable concepts. The system quantizes concepts during learning to ensure that the concepts are readily interpretable as binary activations.
4. Classification Framework: The system employs a straightforward linear classifier that transforms concept activation into class predictions, reinforcing the link between learned concepts and output classes.

Experimental Evaluation

The authors conducted extensive evaluations of BotCL on several datasets, including MNIST, CUB200, ImageNet, and a synthetic shape dataset. These evaluations demonstrated BotCL's capacity to maintain competitive accuracy with existing models, supporting its purpose in improving interpretability without significantly sacrificing classification performance. Of particular note is the strong showing of BotCL in discovering human-interpretable concepts, as evidenced by a user paper and synthetic datasets with ground truth concepts.

Implications and Future Directions

The implications of this work are notable, particularly in fields such as healthcare, where understanding model decisions is critical. The ability to visualize and localize concepts expands the potential for explainable AI systems and paves the way for more transparent decision-making processes. Furthermore, the research opens avenues for further exploration into adaptive determination of the appropriate number of concepts based on the task at hand, which could enhance both the practical relevance and theoretical rigor of concept-based learning models.

In conclusion, this paper offers a promising stride in concept-based interpretability within deep learning, yielding a robust framework that is both computationally efficient and aligned with human interpretative processes. Continued research could enhance the scalability and versatility of such models, especially in multi-modal and complex data environments.