# Kaggle Kinship Recognition Challenge: Introduction of Convolution-Free Model to boost conventional

arXiv: https://arxiv.org/abs/2206.05488 · HTML: https://mlsanity.com/paper/2206.05488/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 11 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [Transformer Interpretability Beyond Attention Visualization](https://mlsanity.com/paper/2012.09838/) (2020)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)

## Abstract (arXiv)

This work aims to explore a convolution-free base classifier that can be used to widen the variations of the conventional ensemble classifier. Specifically, we propose Vision Transformers as base classifiers to combine with CNNs for a unique ensemble solution in Kaggle kinship recognition. In this paper, we verify our proposed idea by implementing and optimizing variants of the Vision Transformer model on top of the existing CNN models. The combined models achieve better scores than conventional ensemble classifiers based solely on CNN variants. We demonstrate that highly optimized CNN ensembles publicly available on the Kaggle Discussion board can easily achieve a significant boost in ROC score by simply ensemble with variants of the Vision Transformer model due to low correlation.