4 citations · 7 across the 5 of their papers we have counts for
5 papers
PULSE: Practical Evaluation Scenarios for Large Multimodal Model Unlearning
Tatsuki Kawakami, Kazuki Egashira, Atsuyuki Miyai +2
In recent years, unlearning techniques, which are methods for inducing a model to "forget" previously learned information, have attracted attention as a way to address privacy and…
A Benchmark and Evaluation for Real-World Out-of-Distribution Detection Using Vision-Language Models
Shiho Noda, Atsuyuki Miyai, Qing Yu +2
Out-of-distribution (OOD) detection is a task that detects OOD samples during inference to ensure the safety of deployed models. However, conventional benchmarks have reached perfo…
Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey
Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang +10
Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine learning systems and has shaped the field of OOD detection. Meanwhile, several other probl…
Unsolvable Problem Detection: Robust Understanding Evaluation for Large Multimodal Models
Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang +7
This paper introduces a novel task to evaluate the robust understanding capability of Large Multimodal Models (LMMs), termed . Multiple…
Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?
Atsuyuki Miyai, Qing Yu, Go Irie +1
Out-of-distribution (OOD) detection is critical for safety-sensitive machine learning applications and has been extensively studied, yielding a plethora of methods developed in the…