Search Results for author: Xin Niu

Found 14 papers, 5 papers with code

TVT: Training-Free Vision Transformer Search on Tiny Datasets

no code implementations24 Nov 2023 Zimian Wei, Hengyue Pan, Lujun Li, Peijie Dong, Zhiliang Tian, Xin Niu, Dongsheng Li

In this paper, for the first time, we investigate how to search in a training-free manner with the help of teacher models and devise an effective Training-free ViT (TVT) search framework.

EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization

1 code implementation ICCV 2023 Peijie Dong, Lujun Li, Zimian Wei, Xin Niu, Zhiliang Tian, Hengyue Pan

In particular, we devise an elaborate search space involving the existing proxies and perform an evolution search to discover the best correlated MQ proxy.


Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks

no code implementations22 May 2023 Haoqi Zheng, Qihuang Zhong, Liang Ding, Zhiliang Tian, Xin Niu, Dongsheng Li, DaCheng Tao

However, most of the mixup methods do not consider the varying degree of learning difficulty in different stages of training and generate new samples with one hot labels, resulting in the model over confidence.

Data Augmentation Few-Shot Text Classification +1

Progressive Meta-Pooling Learning for Lightweight Image Classification Model

no code implementations24 Jan 2023 Peijie Dong, Xin Niu, Zhiliang Tian, Lujun Li, Xiaodong Wang, Zimian Wei, Hengyue Pan, Dongsheng Li

Practical networks for edge devices adopt shallow depth and small convolutional kernels to save memory and computational cost, which leads to a restricted receptive field.

Classification Image Classification

RD-NAS: Enhancing One-shot Supernet Ranking Ability via Ranking Distillation from Zero-cost Proxies

1 code implementation24 Jan 2023 Peijie Dong, Xin Niu, Lujun Li, Zhiliang Tian, Xiaodong Wang, Zimian Wei, Hengyue Pan, Dongsheng Li

In this paper, we propose Ranking Distillation one-shot NAS (RD-NAS) to enhance ranking consistency, which utilizes zero-cost proxies as the cheap teacher and adopts the margin ranking loss to distill the ranking knowledge.

Computational Efficiency Neural Architecture Search

OVO: One-shot Vision Transformer Search with Online distillation

no code implementations28 Dec 2022 Zimian Wei, Hengyue Pan, Xin Niu, Dongsheng Li

OVO samples sub-nets for both teacher and student networks for better distillation results.

Prior-Guided One-shot Neural Architecture Search

1 code implementation27 Jun 2022 Peijie Dong, Xin Niu, Lujun Li, Linzhen Xie, Wenbin Zou, Tian Ye, Zimian Wei, Hengyue Pan

In this paper, we present Prior-Guided One-shot NAS (PGONAS) to strengthen the ranking correlation of supernets.

Neural Architecture Search

Learning Convolutional Neural Networks in the Frequency Domain

2 code implementations14 Apr 2022 Hengyue Pan, Yixin Chen, Xin Niu, Wenbo Zhou, Dongsheng Li

The most important motivation of this research is that we can use the straightforward element-wise multiplication operation to replace the image convolution in the frequency domain based on the Cross-Correlation Theorem, which obviously reduces the computation complexity.

Fixed-size Objects Encoding for Visual Relationship Detection

no code implementations29 May 2020 Hengyue Pan, Xin Niu, Rongchun Li, Siqi Shen, Yong Dou

Instead, we propose a novel method to encode all background objects in each image by using one fixed-size vector (i. e., FBE vector).

General Classification Object +3

STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control

no code implementations28 Aug 2019 Yanan Wang, Tong Xu, Xin Niu, Chang Tan, Enhong Chen, Hui Xiong

Moreover, based on the temporally-dependent traffic information, we design a Graph Neural Network based model to represent relationships among multiple traffic lights, and the decision for each traffic light will be made in a distributed way by the deep Q-learning method.

Management Multi-agent Reinforcement Learning +1

DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networks

no code implementations16 Nov 2018 Hengyue Pan, Hui Jiang, Xin Niu, Yong Dou

Most of previous methods mainly consider to drop features from input data and hidden layers, such as Dropout, Cutout and DropBlocks.

Image Classification

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