# Taming the Randomness: Towards Label-Preserving Cropping in Contrastive Learning

arXiv: https://arxiv.org/abs/2504.19824 · HTML: https://mlsanity.com/paper/2504.19824/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Decoupled Contrastive Learning](https://mlsanity.com/paper/2110.06848/) (2021)
- [Crafting Better Contrastive Views for Siamese Representation Learning](https://mlsanity.com/paper/2202.03278/) (2022)
- [Object-Aware Cropping for Self-Supervised Learning](https://mlsanity.com/paper/2112.00319/) (2021)
- [Unsupervised Visual Representation Learning by Synchronous Momentum Grouping](https://mlsanity.com/paper/2207.06167/) (2022)
- [Fast-MoCo: Boost Momentum-based Contrastive Learning with Combinatorial Patches](https://mlsanity.com/paper/2207.08220/) (2022)

## Abstract (arXiv)

Contrastive learning (CL) approaches have gained great recognition as a very successful subset of self-supervised learning (SSL) methods. SSL enables learning from unlabeled data, a crucial step in the advancement of deep learning, particularly in computer vision (CV), given the plethora of unlabeled image data. CL works by comparing different random augmentations (e.g., different crops) of the same image, thus achieving self-labeling. Nevertheless, randomly augmenting images and especially random cropping can result in an image that is semantically very distant from the original and therefore leads to false labeling, hence undermining the efficacy of the methods. In this research, two novel parameterized cropping methods are introduced that increase the robustness of self-labeling and consequently increase the efficacy. The results show that the use of these methods significantly improves the accuracy of the model by between 2.7% and 12.4% on the downstream task of classifying CIFAR-10, depending on the crop size compared to that of the non-parameterized random cropping method.