# Extending Contrastive Learning to Unsupervised Redundancy Identification
###### Abstract  
Modern deep neural network (DNN)-based approaches have delivered great performance for computer vision tasks; however, they require a massive annotation cost due to their data-hungry nature. Hence, given a fixed budget and unlabeled examples, improving the quality of examples to be annotated is a clever step to obtain good generalization of DNN. One of key issues that could hurt the quality of examples is the presence of redundancy, in which the most examples exhibit similar visual context (e.g., same background). Redundant examples barely contribute to the performance but rather require additional annotation cost. Hence, prior to the annotation process, identifying redundancy is a key step to avoid unnecessary cost. In this work, we proved that the coreset score based on cosine similarity (cossim) is effective for identifying redundant examples. This is because the collective magnitude of the gradient over redundant examples exhibits a large value compared to the others. As a result, contrastive learning first attempts to reduce the loss of redundancy. Consequently, cossim for the redundancy set exhibited a high value (low coreset score). We first viewed the redundancy identification as the gradient magnitude. In this way, we effectively removed redundant examples from two datasets (KITTI, BDD10K), resulting in a better performance in terms of detection and semantic segmentation. deep learning; redundancy identification; convolutional neural network (CNN); semantic segmentation; object detection +
Footnote †: journal: Medical Image Analysis  
was discovered that redundant examples occupy a relatively large portion of well-known datasets [5]. However, few studies have focused on identifying redundancy, particularly for unlabeled data; thus, this topic remains an issue. The most related topic could be key frame detection, which aims to discover the frame that provides powerful features from a video. [PERSON] et al. [6] recently achieved key frame detection in a self-supervised manner, that is, at zero annotation cost. They verified their method by applying a thorough experiment on an action recognition dataset. However, key frame detection is mostly concentrated on a video in which capturing the correlation between consecutive frames is a key technique; thus, it is an impossible approach for a dataset that consists of still images. Moreover, [PERSON] et al. [7] achieved unsupervised coreset selection using a coreset score established by constrastive learning. Their goal was to identify the subset of unlabelled data, which exhibit high contribution to the performance. Hence, they built a coreset score that measures the level of contribution. Inspired by this work, we conjectured that a low coreset score might be able to capture redundancy. This is because redundant examples would obviously exhibit the low level of contribution, thereby being captured by the coreset score. Hence, we confirmed our conjecture via thorough experiment and theoretical analysis with a simple redundant set, where identical examples are present. Again, the aim of this study is to identify redundant examples from unlabeled datasets based on contrastive learning. Hence, prior to the annotation process, with our approach, redundancy scores are recommended that lead to an avoidance of unnecessary annotations. Our contributions are as follows:  
* We extended the work [7] to identify redundant examples. * We provided a theoretical explanation on the why coreset score established by contrastive learning is effective for capturing redundant examples. * We built two different subsets from the entire unlabeled dataset; one with identified redundant examples removed, the other with examples randomly removed. Additionally, the quality of each subset was evaluated by training the existing DNN model for detection and segmentation tasks. The rest of our paper is structured as follows. Section 2 summarizes recent literature on a topic of identifying redundancy and key-frame detection.