11 papers
Generative Data Transformation: From Mixed to Unified Data
Jiaqing Zhang, Mingjia Yin, Hao Wang +6
Recommendation model performance is intrinsically tied to the quality, volume, and relevance of their training data. To address common challenges like data sparsity and cold start,…
HyperNAS: Enhancing Architecture Representation for NAS Predictor via Hypernetwork
Jindi Lv, Yuhao Zhou, Yuxin Tian +3
Time-intensive performance evaluations significantly impede progress in Neural Architecture Search (NAS). To address this, neural predictors leverage surrogate models trained on pr…
Deploying Models to Non-participating Clients in Federated Learning without Fine-tuning: A Hypernetwork-based Approach
Yuhao Zhou, Jindi Lv, Yuxin Tian +3
Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative learning, yet data heterogeneity remains a critical challenge. While existing metho…
GPS: Distilling Compact Memories via Grid-based Patch Sampling for Efficient Online Class-Incremental Learning
Mingchuan Ma, Yuhao Zhou, Jindi Lv +5
Online class-incremental learning aims to enable models to continuously adapt to new classes with limited access to past data, while mitigating catastrophic forgetting. Replay-base…
Style Quantization for Data-Efficient GAN Training
Jian Wang, Xin Lan, Jizhe Zhou +2
Under limited data setting, GANs often struggle to navigate and effectively exploit the input latent space. Consequently, images generated from adjacent variables in a sparse input…
Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints
Yuhao Zhou, Yuxin Tian, Jindi Lv +5
In the realm of high-frequency data streams, achieving real-time learning within varying memory constraints is paramount. This paper presents Ferret, a comprehensive framework desi…
E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing
Yuhao Zhou, Yuxin Tian, Mingjia Shi +4
The exponential growth in model sizes has significantly increased the communication burden in Federated Learning (FL). Existing methods to alleviate this burden by transmitting com…
Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
Yuxin Tian, Mouxing Yang, Yuhao Zhou +5
Conventional federated learning (FL) heavily depends on high-quality labels, which are often impractical in the real world, leading to the federated label-noise (F-LN) problem. Wor…
MSD: A RG Flow-Based Regularization for GAN Training with Limited Data
Jian Wang, Xin Lan, Yuxin Tian +1
Generative adversarial networks (GANs) have made impressive advances in image generation, but they often require large-scale training data to avoid degradation caused by discrimina…
An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model
Yuxin Tian, Mouxing Yang, Yunfan Li +4
Recent studies applied Parameter Efficient Fine-Tuning techniques (PEFTs) to efficiently narrow the performance gap between pre-training and downstream. There are two important fac…
Federated cINN Clustering for Accurate Clustered Federated Learning
Yuhao Zhou, Minjia Shi, Yuxin Tian +3
Federated Learning (FL) presents an innovative approach to privacy-preserving distributed machine learning and enables efficient crowd intelligence on a large scale. However, a sig…