Visualizing Deep Similarity Networks

by   Abby Stylianou, et al.
Temple University
George Washington University

For convolutional neural network models that optimize an image embedding, we propose a method to highlight the regions of images that contribute most to pairwise similarity. This work is a corollary to the visualization tools developed for classification networks, but applicable to the problem domains better suited to similarity learning. The visualization shows how similarity networks that are fine-tuned learn to focus on different features. We also generalize our approach to embedding networks that use different pooling strategies and provide a simple mechanism to support image similarity searches on objects or sub-regions in the query image.


page 1

page 4

page 5

page 6

page 7

page 8


Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers

Despite the success of fine-tuning pretrained language encoders like BER...

2-gram-based Phonetic Feature Generation for Convolutional Neural Network in Assessment of Trademark Similarity

A trademark is a mark used to identify various commodities. If same or s...

Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images

Automated dermoscopic image analysis has witnessed rapid growth in diagn...

Image Colorization Using a Deep Convolutional Neural Network

In this paper, we present a novel approach that uses deep learning techn...

SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation

One-shot semantic segmentation poses a challenging task of recognizing t...

Visualization approach to assess the robustness of neural networks for medical image classification

The use of neural networks for diagnosis classification is becoming more...

Similarity of Neural Network Models: A Survey of Functional and Representational Measures

Measuring similarity of neural networks has become an issue of great imp...

Code Repositories

Please sign up or login with your details

Forgot password? Click here to reset