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An Object Pseudo-Label Generation Technique based on Self-Supervised Vision Transformer for Improving Dataset Quality
Dohyun Kim, Jiwoong Jeon, Seongtaek Lim, Hongchul Lee
http://doi.org/10.5626/JOK.2024.51.1.49
Image segmentation is one of the most important tasks. It localizes objects into bounding boxes and classifies pixels in an image. The performance of an Instance segmentation model requires datasets with labels for objects of various sizes. However, the recently released "Image for Small Object Detection" dataset has large and common objects that lack labels, causing potential performance degradation. In this paper, we improve the quality of datasets by generating pseudo-labels for general objects using an unsupervised learning-based pseudo-labeling methodology to solve the aforementioned problems. Specifically, small object detection performance was improved by (+2.54 AP) compared to the original dataset. Moreover, we were able to prove an increase in performance using only a small amount of data. As a result, it was confirmed that the quality of the dataset was improved through the proposed method.
Deep Ensemble Network with Explicit Complementary Model for Accuracy-balanced Classification
http://doi.org/10.5626/JOK.2019.46.9.941
One of the major evaluation metrics for classification systems is average accuracy, while accuracy deviation is another important performance metric used to evaluate various deep neural networks. In this paper, we present a new ensemble-like fast deep neural network, Harmony, that can reduce the accuracy deviation among categories without degrading the overall average accuracy. Harmony consists of three sub-models: the Target model, Complementary model, and Conductor model. In Harmony, an object is classified by using either the Target model or the Complementary model. The Target model is a conventional classification network for general categories, while the Complementary model is a classification network specifically for weak categories that are inaccurately classified by the Target model. The Conductor model is used to select one of the two models. The experimental results indicate that Harmony accurately classifies categories and also, reduces the accuracy deviation among the categories.
Improving the Lifetime of NAND Flash-based Storages by Min-hash Assisted Delta Compression Engine
Hyoukjun Kwon, Dohyun Kim, Jisung Park, Jihong Kim
In this paper, we propose the Min-hash Assisted Delta-compression Engine(MADE) to improve the lifetime of NAND flash-based storages at the device level. MADE effectively reduces the write traffic to NAND flash through the use of a novel delta compression scheme. The delta compression performance was optimized by introducing min-hash based LSH(Locality Sensitive Hash) and efficiently combining it with our delta compression method. We also developed a delta encoding technique that has functionality equivalent to deduplication and lossless compression. The results of our experiment show that MADE reduces the amount of data written on NAND flash by up to 90%, which is better than a simple combination of deduplication and lossless compression schemes by 12% on average.
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