1 code implementation • 24 Dec 2023 • Dasol Choi, Dongbin Na
With extensive experiments with CIFAR10 and CIFAR100 benchmarks that have been largely adopted in out-of-distribution detection fields, we have demonstrated our MIM shows comprehensively superior performance compared to the SOTA method.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
1 code implementation • 3 Nov 2023 • Beomjune Kim, Eunsun Lee, Dongbin Na
In this work, we focus on automatically recognizing the political intents of a given online newspaper by understanding the context of the text.
1 code implementation • 3 Nov 2023 • Dasol Choi, Dongbin Na
Recently, various studies have presented machine unlearning algorithms and evaluated their methods on several datasets.
1 code implementation • 15 Oct 2023 • JinWoo Seo, Soora Choi, Eungyeom Ha, Beomjune Kim, Dongbin Na
In this paper, we also analyze the robustness performance against hard case samples of large-scale foundation models when we fine-tune the foundation models on the normal cases of the proposed dataset, KoIn.
1 code implementation • 9 Oct 2023 • Juntae Kim, Eunjung Cho, Dongwoo Kim, Dongbin Na
Moreover, we also consider predicting the difficulty levels of algorithm problems, which can be used as useful guidance to calculate the required time to solve that problem.
1 code implementation • 8 Oct 2023 • Eungyeom Ha, Heemook Kim, Sung Chul Hong, Dongbin Na
We present a new benchmark dataset for harmful object detection.
1 code implementation • 6 Oct 2023 • Dasol Choi, Jooyoung Song, Eunsun Lee, JinWoo Seo, Heejune Park, Dongbin Na
With the growth of online services, the need for advanced text classification algorithms, such as sentiment analysis and biased text detection, has become increasingly evident.
no code implementations • 18 Jul 2023 • Jaeyoung Kim, Kyuheon Jung, Dongbin Na, Sion Jang, Eunbin Park, Sungchul Choi
The surrogate OOD sample introduced by POE shows a similar representation to ID data, which is most effective in training a rejection network.
no code implementations • 29 Mar 2023 • Hong-Jun Choi, Dongbin Na, Kyungjin Cho, Byunguk Bae, Seo Taek Kong, Hyunjoon An
This study presents a novel approach to bone age assessment (BAA) using a multi-view, multi-task classification model based on the Sauvegrain method.
1 code implementation • 13 Feb 2023 • Jaeyoung Kim, Dongbin Na, Sungchul Choi, Sungbin Lim
We find that the ensemble model overfitted to the training set shows sub-par calibration performance and also observe that PLMs trained with confidence penalty loss have a trade-off between calibration and accuracy.
1 code implementation • 26 Dec 2022 • Jaeyoung Kim, Seo Taek Kong, Dongbin Na, Kyu-Hwan Jung
We first deduce that OOD images are perceived by a deep neural network to be semantically similar to in-distribution samples when they share a common background, as deep networks are observed to incorrectly classify such images with high confidence.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
1 code implementation • 24 Aug 2022 • Dongbin Na, Sangwoo Ji, Jong Kim
First, we demonstrate that our targeted attack method is query-efficient to produce unrestricted adversarial examples for a facial identity recognition model that contains 307 identities.
no code implementations • 8 Apr 2021 • Seo Taek Kong, Soomin Jeon, Dongbin Na, Jaewon Lee, Hong-Seok Lee, Kyu-Hwan Jung
Although unlabeled data is readily available in pool-based AL, AL algorithms are usually evaluated by measuring the increase in supervised learning (SL) performance at consecutive acquisition steps.