19 citations · 127 across the 127 of their papers we have counts for
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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…
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…
Efficient Multi-Model Fusion with Adversarial Complementary Representation Learning
Zuheng Kang, Yayun He, Jianzong Wang +2
Single-model systems often suffer from deficiencies in tasks such as speaker verification (SV) and image classification, relying heavily on partial prior knowledge during decision-…
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…
Machine Unlearning Methodology base on Stochastic Teacher Network
Xulong Zhang, Jianzong Wang, Ning Cheng +3
The rise of the phenomenon of the "right to be forgotten" has prompted research on machine unlearning, which grants data owners the right to actively withdraw data that has been us…
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…
Machine Unlearning Method Based On Projection Residual
Zihao Cao, Jianzong Wang, Shijing Si +2
Machine learning models (mainly neural networks) are used more and more in real life. Users feed their data to the model for training. But these processes are often one-way. Once t…
RL-MD: A Novel Reinforcement Learning Approach for DNA Motif Discovery
Wen Wang, Jianzong Wang, Shijing Si +2
The extraction of sequence patterns from a collection of functionally linked unlabeled DNA sequences is known as DNA motif discovery, and it is a key task in computational biology.…
A Fair Federated Learning Framework With Reinforcement Learning
Yaqi Sun, Shijing Si, Jianzong Wang +3
Federated learning (FL) is a paradigm where many clients collaboratively train a model under the coordination of a central server, while keeping the training data locally stored. H…
Augmentation-induced Consistency Regularization for Classification
Jianhan Wu, Shijing Si, Jianzong Wang +1
Deep neural networks have become popular in many supervised learning tasks, but they may suffer from overfitting when the training dataset is limited. To mitigate this, many resear…
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…
Efficient Client Contribution Evaluation for Horizontal Federated Learning
Jie Zhao, Xinghua Zhu, Jianzong Wang +1
In federated learning (FL), fair and accurate measurement of the contribution of each federated participant is of great significance. The level of contribution not only provides a…
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…
FedSmart: An Auto Updating Federated Learning Optimization Mechanism
Anxun He, Jianzong Wang, Zhangcheng Huang +1
Federated learning has made an important contribution to data privacy-preserving. Many previous works are based on the assumption that the data are independently identically distri…