5 citations · 8 across the 6 of their papers we have counts for
6 papers
Exemplar-Free Class Incremental Learning via Incremental Representation
Libo Huang, Zhulin An, Yan Zeng +3
Exemplar-Free Class Incremental Learning (efCIL) aims to continuously incorporate the knowledge from new classes while retaining previously learned information, without storing any…
DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction
Chengqing Yu, Fei Wang, Zezhi Shao +3
Multivariate time series long-term prediction, which aims to predict the change of data in a long time, can provide references for decision-making. Although transformer-based model…
Attacking Pre-trained Recommendation
Yiqing Wu, Ruobing Xie, Zhao Zhang +5
Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained…
eTag: Class-Incremental Learning with Embedding Distillation and Task-Oriented Generation
Libo Huang, Yan Zeng, Chuanguang Yang +3
Class-Incremental Learning (CIL) aims to solve the neural networks' catastrophic forgetting problem, which refers to the fact that once the network updates on a new task, its perfo…
Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting
Zezhi Shao, Zhao Zhang, Fei Wang +2
Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly po…
MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition
Chuanguang Yang, Zhulin An, Helong Zhou +5
Unlike the conventional Knowledge Distillation (KD), Self-KD allows a network to learn knowledge from itself without any guidance from extra networks. This paper proposes to perfor…