106 citations · 377 across the 79 of their papers we have counts for
5 papers · 1 filter
Online Continual Learning via Logit Adjusted Softmax
Zhehao Huang, Tao Li, Chenhe Yuan +2
Online continual learning is a challenging problem where models must learn from a non-stationary data stream while avoiding catastrophic forgetting. Inter-class imbalance during tr…
Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels
Fan He, Mingzhen He, Lei Shi +2
The lack of sufficient flexibility is the key bottleneck of kernel-based learning that relies on manually designed, pre-given, and non-trainable kernels. To enhance kernel flexibil…
Low-Dimensional Gradient Helps Out-of-Distribution Detection
Yingwen Wu, Tao Li, Xinwen Cheng +2
Detecting out-of-distribution (OOD) samples is essential for ensuring the reliability of deep neural networks (DNNs) in real-world scenarios. While previous research has predominan…
Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss Landscape Perspective
Kun Fang, Qinghua Tao, Xiaolin Huang +1
Existing Out-of-Distribution (OoD) detection methods address to detect OoD samples from In-Distribution (InD) data mainly by exploring differences in features, logits and gradients…
Multi-Frame Self-Supervised Depth Estimation with Multi-Scale Feature Fusion in Dynamic Scenes
Jiquan Zhong, Xiaolin Huang, Xiao Yu
Multi-frame methods improve monocular depth estimation over single-frame approaches by aggregating spatial-temporal information via feature matching. However, the spatial-temporal…