Y Ming, Z Cai, J Gu, Y Sun, W Li… - Advances in neural …, 2022 - proceedings.neurips.cc
Recognizing out-of-distribution (OOD) samples is critical for machine learning systems deployed in the open world. The vast majority of OOD detection methods are driven by a …
X Du, Y Sun, J Zhu, Y Li - Advances in Neural Information …, 2024 - proceedings.neurips.cc
Utilizing auxiliary outlier datasets to regularize the machine learning model has demonstrated promise for out-of-distribution (OOD) detection and safe prediction. Due to the …
Federated learning (FL) typically faces data heterogeneity, ie, distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data …
J Dong, D Zhang, Y Cong, W Cong… - Proceedings of the …, 2023 - openaccess.thecvf.com
Federated learning-based semantic segmentation (FSS) has drawn widespread attention via decentralized training on local clients. However, most FSS models assume categories …
The inherent challenge of multimodal fusion is to precisely capture the cross-modal correlation and flexibly conduct cross-modal interaction. To fully release the value of each …
Open-world classification systems should discern out-of-distribution (OOD) data whose labels deviate from those of in-distribution (ID) cases, motivating recent studies in OOD …
J Dong, W Liang, Y Cong… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Class-incremental learning (CIL) has achieved remarkable successes in learning new classes consecutively while overcoming catastrophic forgetting on old categories. However …
Abstract Out-of-distribution (OOD) detection is a rapidly growing field due to new robustness and security requirements driven by an increased number of AI-based systems. Existing …
Abstract Out-of-distribution (OOD) detection discerns OOD data where the predictor cannot make valid predictions as in-distribution (ID) data, thereby increasing the reliability of open …