5 papers
Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation
Kun Fang, Qinghua Tao, Mingzhen He +6
The paper proposes a kernel PCA based method for out-of-distribution detection that learns a discriminative non-linear subspace using a newly designed Cosine-Gaussian kernel and in…
Data Imputation by Pursuing Better Classification: A Supervised Kernel-Based Method
Ruikai Yang, Fan He, Mingzhen He +2
Data imputation, the process of filling in missing feature elements for incomplete data sets, plays a crucial role in data-driven learning. A fundamental belief is that data imputa…
MUSO: Achieving Exact Machine Unlearning in Over-Parameterized Regimes
Ruikai Yang, Mingzhen He, Zhengbao He +2
Machine unlearning (MU) is to make a well-trained model behave as if it had never been trained on specific data. In today's over-parameterized models, dominated by neural networks,…
T2I-ConBench: Text-to-Image Benchmark for Continual Post-training
Zhehao Huang, Yuhang Liu, Yixin Lou +7
Continual post-training adapts a single text-to-image diffusion model to learn new tasks without incurring the cost of separate models, but naive post-training causes forgetting of…
Kernel PCA for Out-of-Distribution Detection
Kun Fang, Qinghua Tao, Kexin Lv +3
Out-of-Distribution (OoD) detection is vital for the reliability of Deep Neural Networks (DNNs). Existing works have shown the insufficiency of Principal Component Analysis (PCA) s…