# Categorical Mixture Models on VGGNet activations

Source: [Categorical Mixture Models on VGGNet activations](<https://www.sota2.com/research/paper/68fa64281e209131cea02fa8>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:57:11.341Z

Authors: Sean Billings

Published: 2018-03-06T22:20:10.000Z

## Abstract

In this project, I use unsupervised learning techniques in order to cluster a set of yelp restaurant photos under meaningful topics. In order to do this, I extract layer activations from a pre-trained implementation of the popular VGGNet convolutional neural network. First, I explore using LDA with the activations of convolutional layers as features. Secondly, I explore using the object-recognition powers of VGGNet trained on ImageNet in order to extract meaningful objects from the photos, and then perform LDA to group the photos under topic-archetypes. I find that this second approach finds meaningful archetypes, which match the human intuition for photo topics such as restaurant, food, and drinks. Furthermore, these clusters align well and distinctly with the actual yelp photo labels.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1803.02446>)

[PDF](<https://arxiv.org/pdf/1803.02446.pdf>)