# Analyzing Learned Convnet Features with Dirichlet Process Gaussian Mixture Models

## TL;DR

This paper introduces a clustering-based visualization technique for understanding high-dimensional learned features in convolutional neural networks using Dirichlet Process Gaussian Mixture Models, aiding interpretability and transfer learning.

## Contribution

The paper proposes a novel clustering method for visualizing Convnet features that handles high dimensionality and enhances understanding of learned representations.

## Key findings

- Effective clustering of internal Convnet features across layers
- Improved interpretability of learned representations
- Insights into transfer learning applications

## Abstract

Convolutional Neural Networks (Convnets) have achieved good results in a range of computer vision tasks the recent years. Though given a lot of attention, visualizing the learned representations to interpret Convnets, still remains a challenging task. The high dimensionality of internal representations and the high abstractions of deep layers are the main challenges when visualizing Convnet functionality. We present in this paper a technique based on clustering internal Convnet representations with a Dirichlet Process Gaussian Mixture Model, for visualization of learned representations in Convnets. Our method copes with the high dimensionality of a Convnet by clustering representations across all nodes of each layer. We will discuss how this application is useful when considering transfer learning, i.e.\ transferring a model trained on one dataset to solve a task on a different one.