Understanding intermediate layers using linear classifier probes (1610.01644v4)

Abstract: Neural network models have a reputation for being black boxes. We propose to monitor the features at every layer of a model and measure how suitable they are for classification. We use linear classifiers, which we refer to as "probes", trained entirely independently of the model itself. This helps us better understand the roles and dynamics of the intermediate layers. We demonstrate how this can be used to develop a better intuition about models and to diagnose potential problems. We apply this technique to the popular models Inception v3 and Resnet-50. Among other things, we observe experimentally that the linear separability of features increase monotonically along the depth of the model.

• The paper introduces linear classifier probes to quantitatively assess intermediate representations without altering network training.
• Experiments demonstrate monotonically improved linear separability from shallow to deep layers in models like ResNet-50 and Inception v3.
• The methodology provides a diagnostic tool to identify architectural issues and guide model refinements for better interpretability.

Understanding Intermediate Layers using Linear Classifier Probes

This paper introduces a methodology to elucidate the intermediate layers of neural networks by using linear classifiers termed as "probes." These probes are employed to quantitatively assess the linear separability of features at various depths of models, allowing for a better understanding of layer-wise dynamics without influencing the model's parameters or behavior.

Introduction

The work focuses on addressing the black-box nature of neural networks by introducing probes that measure the classification capability of features at each layer independently of the model's training process. This is achieved through linear classifiers trained separately from the model itself, providing insights into network internals and aiding in the diagnosis of potential issues.

The technique is applied to well-known architectures like Inception v3 and ResNet-50, revealing that the linear separability of the features tends to increase monotonically with network depth. This observation highlights a structural property of deep learning models where deeper layers capture increasingly abstract representations conducive to linear classification.

Linear Classifier Probes

Probes, as conceptualized in the paper, are linear classifiers inserted at various layers of a neural network. These classifiers estimate the ease of classifying outputs from those layers relative to the final output class, thereby gauging the quality of features extracted at different stages.

The insertion of probes allows for a non-invasive method to assess intermediate feature representations. Notably, probe accuracy declines smoothly across layers, suggesting a continuous and predictable development of abstract representations. This tool provides researchers a mechanism to understand intermediate feature transformations without perturbing the network's learning process.

Experiments and Results

The empirical evaluation on MNIST and ImageNet datasets demonstrates the efficacy of probes:

• MNIST: Through adding probes to an elementary convolutional model, the study finds that some layers provide useful transformations even when untrained, as evidenced by a decrease in classification error from 8% to 2% at the ReLU layer.
• ResNet-50: Probes show decreasing error rates across layers, confirming the monotonic improvement of linear separability in this architecture. This monotonic behavior suggests a layer-wise refinement of feature representations.
• Inception v3: The study reveals that auxiliary branches in models like Inception v3 marginally lead the model during early training phases, as evident through slightly improved separability as inspected by probes.

Applications in Diagnostics

Probes are beneficial for diagnosing non-obvious issues in neural network architectures. An illustrative pathological scenario with excessive skip connections highlights how probes diagnosed a "dead segment," where part of the network remained unused despite seemingly successful training. Such insights are invaluable for navigating architectural choices and understanding complex training dynamics.

Discussion on Probe Utility and Future Prospects

The flexibility and diagnostic power of probes encourage further exploration across various model types, including RNNs and GANs. Possible extensions, like multi-layer probes, present an opportunity to capture richer layer interactions, although these introduce complexities, such as the loss of convexity in problem formulation.

Moreover, leveraging the monotonic improvements noted with probes could inspire novel approaches in model training and refinement, particularly in enforcing constraints on linear separability to encourage richer intermediate representations.

Conclusion

Linear probes serve as a powerful augmentation to traditional neural network analysis, providing a window into the often obscure inner workings of deep models. By illustrating consistent enhancements in feature utility across layers, they offer a valuable diagnostic and conceptual aid. This methodological approach holds promise for advancing our understanding of neural network dynamics, ultimately benefiting architectural innovation and model interpretability.