# Image similarity using Deep CNN and Curriculum Learning

arXiv: https://arxiv.org/abs/1709.08761 · HTML: https://mlsanity.com/paper/1709.08761/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2017.

## Built on by (most cited first, 9 of 9)

- [TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment](https://mlsanity.com/paper/2108.09980/) (2021)
- [Place recognition survey: An update on deep learning approaches](https://mlsanity.com/paper/2106.10458/) (2021)
- [Vi(E)va LLM! A Conceptual Stack for Evaluating and Interpreting Generative AI-based Visualizations](https://mlsanity.com/paper/2402.02167/) (2024)
- [A Lightweight Measure of Classification Difficulty from Application Dataset Characteristics](https://mlsanity.com/paper/2404.05981/) (2024)
- [Explainable Image Similarity: Integrating Siamese Networks and Grad-CAM](https://mlsanity.com/paper/2310.07678/) (2023)
- [Multi-step feature fusion for natural disaster damage assessment on satellite images](https://mlsanity.com/paper/2410.21901/) (2024)
- [Identical Image Retrieval using Deep Learning](https://mlsanity.com/paper/2205.04883/) (2022)
- [semantic neural model approach for face recognition from sketch](https://mlsanity.com/paper/2305.01058/) (2023)
- [Efficient Neural Network Model Selection for Few-Class Application Datasets](https://mlsanity.com/paper/2606.19712/) (2026)

## Abstract (arXiv)

Image similarity involves fetching similar looking images given a reference image. Our solution called SimNet, is a deep siamese network which is trained on pairs of positive and negative images using a novel online pair mining strategy inspired by Curriculum learning. We also created a multi-scale CNN, where the final image embedding is a joint representation of top as well as lower layer embedding's. We go on to show that this multi-scale siamese network is better at capturing fine grained image similarities than traditional CNN's.