Aligning Machine and Human Visual Representations across Abstraction Levels (2409.06509v3)

Abstract: Deep neural networks have achieved success across a wide range of applications, including as models of human behavior in vision tasks. However, neural network training and human learning differ in fundamental ways, and neural networks often fail to generalize as robustly as humans do, raising questions regarding the similarity of their underlying representations. What is missing for modern learning systems to exhibit more human-like behavior? We highlight a key misalignment between vision models and humans: whereas human conceptual knowledge is hierarchically organized from fine- to coarse-scale distinctions, model representations do not accurately capture all these levels of abstraction. To address this misalignment, we first train a teacher model to imitate human judgments, then transfer human-like structure from its representations into pretrained state-of-the-art vision foundation models. These human-aligned models more accurately approximate human behavior and uncertainty across a wide range of similarity tasks, including a new dataset of human judgments spanning multiple levels of semantic abstractions. They also perform better on a diverse set of machine learning tasks, increasing generalization and out-of-distribution robustness. Thus, infusing neural networks with additional human knowledge yields a best-of-both-worlds representation that is both more consistent with human cognition and more practically useful, thus paving the way toward more robust, interpretable, and human-like artificial intelligence systems.

Aligning Machine and Human Visual Representations across Abstraction Levels

The paper "Aligning Machine and Human Visual Representations across Abstraction Levels" explores the notion of enhancing the alignment between neural network representations and human cognitive representations, particularly in the domain of visual perception. This research stands on the intersection of cognitive science and artificial intelligence, aiming to bridge the gap between the inherently hierarchical human conceptual knowledge and the representations learned by deep neural networks through various training paradigms.

Key Points and Methodology

One of the pivotal observations made in the paper is that despite the success of deep learning models in various applications, these models often fail in ways humans do not, particularly due to the differences in the ways both systems learn and generalize visual concepts. Human conceptual knowledge is hierarchically organized, allowing for robust generalization across different levels of abstraction—ranging from fine-scale distinctions to broader categorical structures. In contrast, neural network models tend to falter in capturing this multi-level abstraction hierarchy, limiting their robustness and generalization capabilities.

Methodological Framework

To address this misalignment, the authors propose a multi-step framework involving several novel components:

1. Teacher Model Training:
   • A teacher model is trained to imitate human judgments on visual similarity tasks. The THINGS dataset, capturing human responses in a triplet odd-one-out task, serves as the primary data source for aligning this model with human cognitive representations.
   • The authors introduce an affine transformation that maps the model’s representation space to align with human similarity judgments, incorporating uncertainty measures from human responses obtained via Variational Interpretable Concept Embeddings (VICE).
2. Triplet Generation:
   • Utilizing the aligned teacher model, a large dataset of human-like triplet odd-one-out responses, termed AligNet, is generated. This involves clustering the transformed representations into semantically meaningful superordinate categories to guide the generation of informative triplets.
3. Student Model Alignment:
   • The generated AligNet dataset is used to fine-tune various student vision foundation models (VFMs) using a novel Kullback-Leibler divergence based objective function. This loss function is designed to distill the hierarchical human-like similarity structure into the student models’ representations.

Results and Implications

The empirical evaluations demonstrate that AligNet fine-tuning substantially improves the alignment between neural network models and human judgments across multiple cognitive tasks and datasets. These improvements are consistent across various model architectures and pretraining objectives:

• Triplet Odd-One-Out Accuracy: AligNet models show a significant increase in the odd-one-out accuracy on the THINGS dataset, with the best-performing model achieving 62.54%.
• Multi-Arrangement and Pairwise Similarity Tasks: The fine-tuned models exhibit higher Spearman rank correlations with human similarity judgments in datasets collected via multi-arrangement tasks and pairwise similarity ratings.
• Hierarchical Conceptual Knowledge: The alignment procedure effectively organizes model representations to reflect human-like hierarchical conceptual structures, capturing both global and local semantic relationships.

Furthermore, the paper introduces and evaluates a novel dataset named Levels, which specifically measures model performance across different levels of abstraction. The findings indicate that the largest misalignment—and consequently the most significant improvements post-alignment—occurs at the global coarse-grained semantic level. This highlights the critical role of incorporating broad categorical structures into model training to achieve robust human-like perception.

Practical and Theoretical Implications

From a practical perspective, the enhanced alignment between human and machine representations holds promise for developing AI systems that generalize better and are more robust to distributional shifts in data. This could lead to significant advancements in applications that require high levels of generalization and robustness, such as autonomous driving, medical image analysis, and any interactive AI system relying on human-like perception.

Theoretically, the results contribute to ongoing debates about the capabilities of neural networks to capture human-like intelligence. The research underscores the importance of incorporating human cognitive structures into AI models to overcome some of their inherent limitations. Future directions may include expanding this alignment framework to other domains, such as language processing, where hierarchical structures are equally prevalent.

In conclusion, the paper provides a comprehensive approach to aligning machine and human visual representations, demonstrating that infusing neural networks with human cognitive structures significantly enhances their performance and consistency with human behavior. This work paves the way for creating more robust, interpretable, and human-like artificial intelligence systems, contributing both practical benefits and theoretical insights into the development of next-generation AI.