Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition (1508.03929v4)

Abstract: Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases. Their architecture is somewhat similar to that of the human visual system: both use restricted receptive fields, and a hierarchy of layers which progressively extract more and more abstracted features. Yet it is unknown whether DCNNs match human performance at the task of view-invariant object recognition, whether they make similar errors and use similar representations for this task, and whether the answers depend on the magnitude of the viewpoint variations. To investigate these issues, we benchmarked eight state-of-the-art DCNNs, the HMAX model, and a baseline shallow model and compared their results to those of humans with backward masking. Unlike in all previous DCNN studies, we carefully controlled the magnitude of the viewpoint variations to demonstrate that shallow nets can outperform deep nets and humans when variations are weak. When facing larger variations, however, more layers were needed to match human performance and error distributions, and to have representations that are consistent with human behavior. A very deep net with 18 layers even outperformed humans at the highest variation level, using the most human-like representations.

• The paper finds that deeper deep convolutional neural networks (DCNNs) increasingly resemble human feed-forward vision in invariant object recognition, with an 18-layer network exceeding human performance at the highest level of variation.
• Some DCNNs exhibit misclassification patterns and internal representational geometries comparable to human vision, suggesting deeper alignment at behavioral output and neural levels.
• Layer-wise analysis highlights how hierarchical processing in later DCNN layers mirrors human visual processing stages, offering insights into beneficial architectural decisions like using more layers and smaller filters.

Deep Networks and Human Vision: Evaluating Invariant Object Recognition

The paper "Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition" undertakes a comprehensive examination of deep convolutional neural networks (DCNNs) and their capability to mimic human feed-forward visual processing, particularly in invariant object recognition tasks. The paper aims to address the alignment between DCNNs and human visual performance, focusing on whether these models can achieve or surpass human-like performance and how they handle variances in visual inputs.

The researchers compare eight state-of-the-art DCNNs, the HMAX model, and a simple pixel-based shallow model against human performance in object categorization tasks. The image database used in the paper was meticulously constructed to include objects varying along five parameters: size, position, rotation in-plane, rotation in-depth, and background complexity. This rigorous setup allowed the researchers to assess model performance across varying levels of difficulty, indicated as levels of variation.

Key Findings

1. Performance and Accuracy: The results suggested that deeper networks generally perform better in handling higher levels of variation, aligning more closely with human accuracy. The included DCNNs outperformed the shallow HMAX model and demonstrated significant improvements over a purely pixel-driven model, particularly in challenging scenarios with substantial viewpoint changes. Specifically, a very deep network with 18 layers managed to exceed human performance at the highest level of variation.
2. Error Distribution and Representational Accuracy: Unlike prior studies, the researchers focused on error distribution through confusion matrices to identify if models made similar misclassification errors as humans. Notably, some DCNNs exhibited misclassification patterns comparable to human observers under challenging conditions, demonstrating a deeper alignment at the behavioral output level.
3. Layer-Specific Analysis: The paper's comprehensive layer-wise analysis highlights how invariant representations evolve through successive layers of DCNNs. Findings indicate that the benefits from hierarchical processing become evident in later layers, paralleling processing stages in the human ventral visual stream.
4. Representational Dissimilarity Structure: Using representational similarity analysis, there is evidence that certain DCNN architectures (notably the Zeiler and Fergus model among others) contain internal representational geometries more consistent with human IT cortical areas, although performance remained distinctively task-dependent.

Implications and Future Research

The findings underscore the potential of DCNNs to not only reach but sometimes surpass human performance in invariant object recognition—though this arises primarily from feed-forward processes. The research suggests crucial architectural decisions, such as the incorporation of more convolutional layers and smaller filter sizes, can materially influence network efficacy.

From a theoretical perspective, this work bridges understanding between neuroscience and machine learning, suggesting that the hierarchical feed-forward mechanisms observed in primate vision can be effectively modeled to improve machine vision systems. However, the paper also points out significant areas for refinement. Current DCNNs lack feedback mechanisms akin to those in biological systems, potentially hindered by the absence of innate figure-ground segregation or attentional modulation. Further exploration into network architectures that can emulate these aspects of human vision through recurrent processes or integrated attention mechanisms may offer untapped pathways for advancement.

In conclusion, this paper provides vital empirical insights into the intersection of artificial neural networks and human vision. The implications of these findings extend into both the optimization of machine learning algorithms and our broader understanding of visual cognition. Future developments in AI may benefit from this synthesis, encouraging design philosophies that integrate both feed-forward robustness and flexible, context-sensitive processing capabilities found within human neural architectures.