903 citations · 2.5k across the 78 of their papers we have counts for
19 papers · 1 filter
Exploring Category-correlated Feature for Few-shot Image Classification
Jing Xu, Xinglin Pan, Xu Luo +2
Few-shot classification aims to adapt classifiers to novel classes with a few training samples. However, the insufficiency of training data may cause a biased estimation of feature…
Self-Paced Deep Regression Forests with Consideration of Ranking Fairness
Lili Pan, Mingming Meng, Yazhou Ren +2
Deep discriminative models (DDMs), e.g. deep regression forests and deep decision forests, have been extensively studied recently to solve problems such as facial age estimation, h…
Label-Aware Distribution Calibration for Long-tailed Classification
Chaozheng Wang, Shuzheng Gao, Cuiyun Gao +4
Real-world data usually present long-tailed distributions. Training on imbalanced data tends to render neural networks perform well on head classes while much worse on tail classes…
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs
Langzhang Liang, Cuiyun Gao, Shiyi Chen +5
Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, l…
Boosting Few-Shot Classification with View-Learnable Contrastive Learning
Xu Luo, Yuxuan Chen, Liangjian Wen +2
The goal of few-shot classification is to classify new categories with few labeled examples within each class. Nowadays, the excellent performance in handling few-shot classificati…
FedLab: A Flexible Federated Learning Framework
Dun Zeng, Siqi Liang, Xiangjing Hu +2
Federated learning (FL) is a machine learning field in which researchers try to facilitate model learning process among multiparty without violating privacy protection regulations.…
Stein Variational Gradient Descent with Multiple Kernel
Qingzhong Ai, Shiyu Liu, Lirong He +1
Stein variational gradient descent (SVGD) and its variants have shown promising successes in approximate inference for complex distributions. In practice, we notice that the kernel…
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE
Qingzhong Ai, Lirong He, Shiyu Liu +1
Recent studies show that advanced priors play a major role in deep generative models. Exemplar VAE, as a variant of VAE with an exemplar-based prior, has achieved impressive result…
Rectifying the Shortcut Learning of Background for Few-Shot Learning
Xu Luo, Longhui Wei, Liangjian Wen +4
The category gap between training and evaluation has been characterised as one of the main obstacles to the success of Few-Shot Learning (FSL). In this paper, we for the first time…
Discrete Auto-regressive Variational Attention Models for Text Modeling
Xianghong Fang, Haoli Bai, Jian Li +3
Variational autoencoders (VAEs) have been widely applied for text modeling. In practice, however, they are troubled by two challenges: information underrepresentation and posterior…
AFINet: Attentive Feature Integration Networks for Image Classification
Xinglin Pan, Jing Xu, Yu Pan +4
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification. Recent advanced models in CNNs, such as ResNets,…
Unsupervised Sentiment Analysis by Transferring Multi-source Knowledge
Yong Dai, Jian Liu, Jian Zhang +2
Sentiment analysis (SA) is an important research area in cognitive computation-thus in-depth studies of patterns of sentiment analysis are necessary. At present, rich resource data…
Pseudo-supervised Deep Subspace Clustering
Juncheng Lv, Zhao Kang, Xiao Lu +1
Auto-Encoder (AE)-based deep subspace clustering (DSC) methods have achieved impressive performance due to the powerful representation extracted using deep neural networks while pr…
TedNet: A Pytorch Toolkit for Tensor Decomposition Networks
Yu Pan, Maolin Wang, Zenglin Xu
Tensor Decomposition Networks (TDNs) prevail for their inherent compact architectures. To give more researchers a flexible way to exploit TDNs, we present a Pytorch toolkit named T…
Partial Differential Equations is All You Need for Generating Neural Architectures -- A Theory for Physical Artificial Intelligence Systems
Ping Guo, Kaizhu Huang, Zenglin Xu
In this work, we generalize the reaction-diffusion equation in statistical physics, Schrödinger equation in quantum mechanics, Helmholtz equation in paraxial optics into the neural…
A Survey on Deep Semi-supervised Learning
Xiangli Yang, Zixing Song, Irwin King +1
Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in…
Graph-based Semi-supervised Learning: A Comprehensive Review
Zixing Song, Xiangli Yang, Zenglin Xu +1
Semi-supervised learning (SSL) has tremendous value in practice due to its ability to utilize both labeled data and unlabelled data. An important class of SSL methods is to natural…
RegNet: Self-Regulated Network for Image Classification
Jing Xu, Yu Pan, Xinglin Pan +3
The ResNet and its variants have achieved remarkable successes in various computer vision tasks. Despite its success in making gradient flow through building blocks, the simple sho…
MultiFace: A Generic Training Mechanism for Boosting Face Recognition Performance
Jing Xu, Tszhang Guo, Yong Xu +2
Deep Convolutional Neural Networks (DCNNs) and their variants have been widely used in large scale face recognition(FR) recently. Existing methods have achieved good performance on…