Measuring the tendency of CNNs to Learn Surface Statistical Regularities (1711.11561v1)

Abstract: Deep CNNs are known to exhibit the following peculiarity: on the one hand they generalize extremely well to a test set, while on the other hand they are extremely sensitive to so-called adversarial perturbations. The extreme sensitivity of high performance CNNs to adversarial examples casts serious doubt that these networks are learning high level abstractions in the dataset. We are concerned with the following question: How can a deep CNN that does not learn any high level semantics of the dataset manage to generalize so well? The goal of this article is to measure the tendency of CNNs to learn surface statistical regularities of the dataset. To this end, we use Fourier filtering to construct datasets which share the exact same high level abstractions but exhibit qualitatively different surface statistical regularities. For the SVHN and CIFAR-10 datasets, we present two Fourier filtered variants: a low frequency variant and a randomly filtered variant. Each of the Fourier filtering schemes is tuned to preserve the recognizability of the objects. Our main finding is that CNNs exhibit a tendency to latch onto the Fourier image statistics of the training dataset, sometimes exhibiting up to a 28% generalization gap across the various test sets. Moreover, we observe that significantly increasing the depth of a network has a very marginal impact on closing the aforementioned generalization gap. Thus we provide quantitative evidence supporting the hypothesis that deep CNNs tend to learn surface statistical regularities in the dataset rather than higher-level abstract concepts.

• The paper demonstrates that CNNs primarily learn superficial statistical regularities instead of high-level abstract concepts, leading to notable generalization gaps.
• It employs Fourier filtering techniques on datasets like SVHN and CIFAR-10 to isolate and test the influence of surface statistics on model performance.
• The findings indicate that increasing network depth does not mitigate this bias, underscoring the need for new training methods focused on concept-driven generalization.

An Examination of CNNs' Tendency Toward Surface Statistical Regularities

The paper "Measuring the tendency of CNNs to Learn Surface Statistical Regularities" authored by Jason Jo and Yoshua Bengio investigates a critical aspect of convolutional neural networks (CNNs): their inclination to prioritize surface statistical regularities over abstract high-level concepts. This paper provides vital insights into the generalization abilities of CNNs, particularly in reconciling their exceptional performance on standard datasets with their sensitivity to adversarial perturbations.

Core Findings and Methodology

At the heart of this paper is the hypothesis that CNNs are predisposed to learning superficial dataset characteristics, specifically the statistical regularities apparent in natural images. The authors argue that such a focus is responsible for CNNs' impressive performance on standard benchmarks and their vulnerability to adversarial examples, which are designed to undermine these very regularities.

To empirically substantiate this claim, the research employs Fourier filtering to construct datasets with preserved high-level semantics but altered surface statistics. The authors use two variants of Fourier filtering on the SVHN and CIFAR-10 datasets: a low frequency and a randomly filtered version, ensuring that object recognizability is human-comprehensible while altering image statistics.

Results Unveiled

The experimental results reveal that CNNs exhibit a marked propensity to capture the Fourier image statistics of their training data. CNNs trained on original datasets show a significant generalization gap—up to 28%—when evaluated on test sets with differing Fourier statistical properties. Increasing the network depth from 92 to 200 layers proves insufficient in closing this generalization gap. These findings reinforce the viewpoint that the current CNN architectures may not effectively learn high-level abstract representations, but instead rely heavily on pervasive low-level statistical cues for generalization.

Implications and Future Directions

The implications of this research are profound for both the theoretical understanding and practical deployment of deep learning systems. The tendency to learn surface-level regularities suggests potential limitations in CNNs' ability to generalize across varying distributions or tasks that differ slightly in statistical makeup from the training dataset.

For future developments in AI, this insight suggests a direction toward designing neural network architectures and training methodologies that can adequately abstract higher-level concepts independent of superficial statistics. The potential strategies include exploring unsupervised learning frameworks that emphasize structure and relational learning, and the use of comprehensive data augmentation techniques akin to adversarial training but aimed at a broader spectrum of statistical variations.

This paper thus positions itself as a crucial piece in advancing the comprehension of CNN behavior, challenging the community to rethink and innovate beyond current practices to realize models capable of robust, concept-driven generalization. In pursuing these paths, researchers could unlock the next frontier of deep learning systems that align more closely with human cognitive abilities.