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Heng Luo

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5

JBHI Journal 2025 Journal Article

Fourier-Based Frequency Space Disentanglement and Augmentation for Generalizable Face Anti-Spoofing

  • Yang Yu
  • Zhekai Du
  • Heng Luo
  • Chengwei Xiao
  • Jiang Hu

Generalizing face anti-spoofing (FAS) models to unseen distributions is challenging due to domain shifts. Previous domain generalization (DG) based FAS methods focus on learning invariant features across domains in the spatial space, which may be ineffective in detecting subtle spoof patterns. In this paper, we propose a novel approach called Frequency Space Disentanglement and Augmentation (FSDA) for generalizable FAS. Specifically, we leverage Fourier transformation to analyze face images in the frequency space, where the amplitude spectrum captures low-level texture information that forms distinct visual appearances, and the phase spectrum corresponds to the content information. We hypothesize that the liveness of a face is more related to these low-level patterns rather than high-level content information. To locate spoof traces, we disentangle the amplitude spectrum into domain-related and spoof-related components using either empirical or learnable strategies. We then propose a frequency space augmentation technique that mixes the disentangled components of two images to synthesize new variations. By imposing a distillation loss and a consistency loss on the augmented samples, our model learns to capture spoof patterns that are robust to both domain and spoof type variations. Extensive experiments on four FAS datasets demonstrate the superiority of our method in improving the generalization ability of FAS models in various unseen scenarios.

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AIIM Journal 2023 Journal Article

DDI-GCN: Drug-drug interaction prediction via explainable graph convolutional networks

  • Yi Zhong
  • Houbing Zheng
  • Xiaoming Chen
  • Yu Zhao
  • Tingfang Gao
  • Huiqun Dong
  • Heng Luo
  • Zuquan Weng

Drug-drug interactions (DDI) may lead to unexpected side effects, which is a growing concern in both academia and industry. Many DDIs have been reported, but the underlying mechanisms are not well understood. Predicting and understanding DDIs can help researchers to improve drug safety and protect patient health. Here, we introduce DDI-GCN, a method that utilizes graph convolutional networks (GCN) to predict DDIs based on chemical structures. We demonstrate that this method achieves state-of-the-art prediction performance on the independent hold-out set. It can also provide visualization of structural features associated with DDIs, which can help us to study the underlying mechanisms. To make it easy and accessible to use, we developed a web server for DDI-GCN, which is freely available at http: //wengzq-lab. cn/ddi/.

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AAAI Conference 2020 Conference Paper

Diversity Transfer Network for Few-Shot Learning

  • Mengting Chen
  • Yuxin Fang
  • Xinggang Wang
  • Heng Luo
  • Yifeng Geng
  • Xinyu Zhang
  • Chang Huang
  • Wenyu Liu

Few-shot learning is a challenging task that aims at training a classifier for unseen classes with only a few training examples. The main difficulty of few-shot learning lies in the lack of intra-class diversity within insufficient training samples. To alleviate this problem, we propose a novel generative framework, Diversity Transfer Network (DTN), that learns to transfer latent diversities from known categories and composite them with support features to generate diverse samples for novel categories in feature space. The learning problem of the sample generation (i. e. , diversity transfer) is solved via minimizing an effective meta-classification loss in a single-stage network, instead of the generative loss in previous works. Besides, an organized auxiliary task co-training over known categories is proposed to stabilize the meta-training process of DTN. We perform extensive experiments and ablation studies on three datasets, i. e. , miniImageNet, CIFAR100 and CUB. The results show that DTN, with single-stage training and faster convergence speed, obtains the state-of-the-art results among the feature generation based few-shot learning methods. Code and supplementary material are available at: https: //github. com/Yuxin-CV/DTN.

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IJCAI Conference 2018 Conference Paper

Interpretable Drug Target Prediction Using Deep Neural Representation

  • Kyle Yingkai Gao
  • Achille Fokoue
  • Heng Luo
  • Arun Iyengar
  • Sanjoy Dey
  • Ping Zhang

The identification of drug-target interactions (DTIs) is a key task in drug discovery, where drugs are chemical compounds and targets are proteins. Traditional DTI prediction methods are either time consuming (simulation-based methods) or heavily dependent on domain expertise (similarity-based and feature-based methods). In this work, we propose an end-to-end neural network model that predicts DTIs directly from low level representations. In addition to making predictions, our model provides biological interpretation using two-way attention mechanism. Instead of using simplified settings where a dataset is evaluated as a whole, we designed an evaluation dataset from BindingDB following more realistic settings where predictions of unobserved examples (proteins and drugs) have to be made. We experimentally compared our model with matrix factorization, similarity-based methods, and a previous deep learning approach. Overall, the results show that our model outperforms other approaches without requiring domain knowledge and feature engineering. In a case study, we illustrated the ability of our approach to provide biological insights to interpret the predictions.

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AAAI Conference 2011 Conference Paper

Sparse Group Restricted Boltzmann Machines

  • Heng Luo
  • Ruimin Shen
  • Changyong Niu
  • Carsten Ullrich

Since learning in Boltzmann machines is typically quite slow, there is a need to restrict connections within hidden layers. However, the resulting states of hidden units exhibit statistical dependencies. Based on this observation, we propose using l1/l2 regularization upon the activation probabilities of hidden units in restricted Boltzmann machines to capture the local dependencies among hidden units. This regularization not only encourages hidden units of many groups to be inactive given observed data but also makes hidden units within a group compete with each other for modeling observed data. Thus, the l1/l2 regularization on RBMs yields sparsity at both the group and the hidden unit levels. We call RBMs trained with the regularizer sparse group RBMs (SGRBMs). The proposed SGRBMs are applied to model patches of natural images, handwritten digits and OCR English letters. Then to emphasize that SGRBMs can learn more discriminative features we applied SGRBMs to pretrain deep networks for classification tasks. Furthermore, we illustrate the regularizer can also be applied to deep Boltzmann machines, which lead to sparse group deep Boltzmann machines. When adapted to the MNIST data set, a two-layer sparse group Boltzmann machine achieves an error rate of 0. 84%, which is, to our knowledge, the best published result on the permutation-invariant version of the MNIST task.

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