13 citations · 31 across the 55 of their papers we have counts for
10 papers · 1 filter
ACCon: Angle-Compensated Contrastive Regularizer for Deep Regression
Botao Zhao, Xiaoyang Qu, Zuheng Kang +3
In deep regression, capturing the relationship among continuous labels in feature space is a fundamental challenge that has attracted increasing interest. Addressing this issue can…
Incremental Label Distribution Learning with Scalable Graph Convolutional Networks
Ziqi Jia, Xiaoyang Qu, Chenghao Liu +1
Label Distribution Learning (LDL) is an effective approach for handling label ambiguity, as it can analyze all labels at once and indicate the extent to which each label describes…
Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training
Zhiyuan Wang, Bokui Chen, Xiaoyang Qu +3
With the rapid advancements in artificial intelligence, the development of knowledgeable and personalized agents has become increasingly prevalent. However, the inherent variabilit…
INCPrompt: Task-Aware incremental Prompting for Rehearsal-Free Class-incremental Learning
Zhiyuan Wang, Xiaoyang Qu, Jing Xiao +2
This paper introduces INCPrompt, an innovative continual learning solution that effectively addresses catastrophic forgetting. INCPrompt's key innovation lies in its use of adaptiv…
P2DT: Mitigating Forgetting in task-incremental Learning with progressive prompt Decision Transformer
Zhiyuan Wang, Xiaoyang Qu, Jing Xiao +2
Catastrophic forgetting poses a substantial challenge for managing intelligent agents controlled by a large model, causing performance degradation when these agents face new tasks.…
GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection
Jinggang Chen, Junjie Li, Xiaoyang Qu +3
Detecting out-of-distribution (OOD) examples is crucial to guarantee the reliability and safety of deep neural networks in real-world settings. In this paper, we offer an innovativ…
FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer
Chenghao Liu, Xiaoyang Qu, Jianzong Wang +1
Federated Learning (FL) has been widely concerned for it enables decentralized learning while ensuring data privacy. However, most existing methods unrealistically assume that the…
Detecting Out-of-distribution Examples via Class-conditional Impressions Reappearing
Jinggang Chen, Xiaoyang Qu, Junjie Li +3
Out-of-distribution (OOD) detection aims at enhancing standard deep neural networks to distinguish anomalous inputs from original training data. Previous progress has introduced va…
Federated Learning with Dynamic Transformer for Text to Speech
Zhenhou Hong, Jianzong Wang, Xiaoyang Qu +3
Text to speech (TTS) is a crucial task for user interaction, but TTS model training relies on a sizable set of high-quality original datasets. Due to privacy and security issues, t…
Enhancing Data-Free Adversarial Distillation with Activation Regularization and Virtual Interpolation
Xiaoyang Qu, Jianzong Wang, Jing Xiao
Knowledge distillation refers to a technique of transferring the knowledge from a large learned model or an ensemble of learned models to a small model. This method relies on acces…