CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters (2203.15331v2)

Abstract: Currently, many theoretical as well as practically relevant questions towards the transferability and robustness of Convolutional Neural Networks (CNNs) remain unsolved. While ongoing research efforts are engaging these problems from various angles, in most computer vision related cases these approaches can be generalized to investigations of the effects of distribution shifts in image data. In this context, we propose to study the shifts in the learned weights of trained CNN models. Here we focus on the properties of the distributions of dominantly used 3x3 convolution filter kernels. We collected and publicly provide a dataset with over 1.4 billion filters from hundreds of trained CNNs, using a wide range of datasets, architectures, and vision tasks. In a first use case of the proposed dataset, we can show highly relevant properties of many publicly available pre-trained models for practical applications: I) We analyze distribution shifts (or the lack thereof) between trained filters along different axes of meta-parameters, like visual category of the dataset, task, architecture, or layer depth. Based on these results, we conclude that model pre-training can succeed on arbitrary datasets if they meet size and variance conditions. II) We show that many pre-trained models contain degenerated filters which make them less robust and less suitable for fine-tuning on target applications. Data & Project website: https://github.com/paulgavrikov/cnn-filter-db

• The paper demonstrates that CNN convolutional filters can degenerate into sparse, low-diversity patterns even in top-performing models.
• It shows that filter distributions remain stable across varied datasets and tasks, supporting robust transfer learning practices.
• The study employs entropy metrics and PCA to quantify filter diversity, offering actionable insights for optimizing model robustness and compression.

An Analysis of CNN Filter DB: Investigating the Structure and Properties of Convolutional Filters

In the landscape of computer vision, Convolutional Neural Networks (CNNs) have become indispensable tools. However, their practical deployment often faces challenges such as robustness against distribution shifts and the need for large annotated datasets. The paper "CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters" presents a comprehensive paper addressing these issues by analyzing the learned convolutional filters of various CNN architectures.

The authors introduce a novel dataset comprising over 1.4 billion $3\times 3$ convolution filters extracted from hundreds of trained CNNs. This extensive dataset encompasses a diverse range of models, architectures, and tasks, providing a broad spectrum for empirical analysis.

Key Findings:

1. Filter Degeneration: The paper highlights the presence of degenerated filters even in robustly performing CNN models. These filters, categorized by high sparsity, low diversity, and randomness, indicate inefficiencies within the network that can arise from overparameterization or insufficient training samples.
2. Impact on Transfer Learning: Interestingly, the research suggests that the distribution of convolutional filters is relatively stable across different image distributions and tasks, including diverse visual categories. This implies potential for successful pre-training across varied datasets, provided they adhere to certain size and variance criteria.
3. Model and Filter Relationships: Through a thorough statistical analysis, the paper reveals that filter structures exhibit minimal shifts across different family architectures or training datasets. This challenges the prevalent notion that dataset similarity significantly impacts the effectiveness of transferred neural network features.
4. Distribution Shift Analyses: Despite low model-to-model shifts within the same family, varying datasets and tasks, some unique divergences are identified, particularly in tasks like GAN-discriminator models likely due to high randomness. The paper suggests that such randomness indicates confusion, making certain models less optimal for generating distinct features.
5. Layer-Specific Insights: Detailed evaluation at layer level reveals that not all layer filters are equally affected by parameterization, while degeneration prominently impacts mid to deeper layers, leading to less diverse, sparse output filters.

Methodological Contributions:

• Entropy-Based Metrics: The paper introduces entropy as a measure to quantify the diversity in filter structures, aiding the identification of degenerated layers.
• Analysis of Principal Component Variance: By applying Principal Component Analysis (PCA), the authors quantify filter diversity that could guide strategies for model compression without loss of critical functionalities.

Practical and Theoretical Implications:

• Implications for Robust Training: The paper underscores the correlation between robust training methods and the development of diverse filters, adding a new dimension to optimizing CNN architecture for specific applications.
• Transfer Learning Optimization: With datasets like ImageNet often used for pre-training, this research supports the notion that effective pre-training can occur across various visual categories, potentially reducing the dependency on large-scale, labeled datasets.

Future Directions:

The insights gleaned set the stage for refined model optimization strategies, including focused pruning and enhanced pre-training methods that emphasize model robustness and efficiency. As a substantial contribution to the understanding of CNN filter dynamics, future work could explore automated generation of such empirical databases targeting specific application contexts within neural network research.

The publication of the CNN Filter DB as an open dataset further promises to facilitate continued exploration and development in the field, providing a valuable benchmark for advancing the scientific discourse in CNN architectures and their application domains.