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Found 13 papers, 10 papers with code
Towards Reliable AI Model Deployments: Multiple Input Mixup for Out-of-Distribution Detection
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
Towards Machine Unlearning Benchmarks: Forgetting the Personal Identities in Facial Recognition Systems
Recently, various studies have presented machine unlearning algorithms and evaluated their methods on several datasets.
A New Korean Text Classification Benchmark for Recognizing the Political Intents in Online Newspapers
In this work, we focus on automatically recognizing the political intents of a given online newspaper by understanding the context of the text.
New Benchmarks for Asian Facial Recognition Tasks: Face Classification with Large Foundation Models
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.
Problem-Solving Guide: Predicting the Algorithm Tags and Difficulty for Competitive Programming Problems
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.
HOD: A Benchmark Dataset for Harmful Object Detection
We present a new benchmark dataset for harmful object detection.
KoMultiText: Large-Scale Korean Text Dataset for Classifying Biased Speech in Real-World Online Services
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.
Pseudo Outlier Exposure for Out-of-Distribution Detection using Pretrained Transformers
The surrogate OOD sample introduced by POE shows a similar representation to ID data, which is most effective in training a rejection network.
Self-accumulative Vision Transformer for Bone Age Assessment Using the Sauvegrain Method
This study presents a novel approach to bone age assessment (BAA) using a multi-view, multi-task classification model based on the Sauvegrain method.
Bag of Tricks for In-Distribution Calibration of Pretrained Transformers
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.
Key Feature Replacement of In-Distribution Samples for Out-of-Distribution Detection
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
Unrestricted Black-box Adversarial Attack Using GAN with Limited Queries
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.
A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity
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.
