no code implementations • 19 Apr 2024 • Yuchen Zhang, Xiaoxiao Ma, Jia Wu, Jian Yang, Hao Fan
To bridge the gap, this work proposes a heterogeneous subgraph transformer (HeteroSGT) to exploit subgraphs in our constructed heterogeneous graph.
no code implementations • 15 Feb 2024 • Yuxuan Gu, Yi Jin, Ben Wang, Zhixiang Wei, Xiaoxiao Ma, Pengyang Ling, Haoxuan Wang, Huaian Chen, Enhong Chen
In this work, we observe that the generators, which are pre-trained on massive natural images, inherently hold the promising potential for superior low-light image enhancement against varying scenarios. Specifically, we embed a pre-trained generator to Retinex model to produce reflectance maps with enhanced detail and vividness, thereby recovering features degraded by low-light conditions. Taking one step further, we introduce a novel optimization strategy, which backpropagates the gradients to the input seeds rather than the parameters of the low-light enhancement model, thus intactly retaining the generative knowledge learned from natural images and achieving faster convergence speed.
1 code implementation • 26 Jan 2024 • Xiaoxiao Ma, Zhixiang Wei, Yi Jin, Pengyang Ling, Tianle Liu, Ben Wang, Junkang Dai, Huaian Chen, Enhong Chen
In this work, we observe that the model, which is trained on vast general images using masking strategy, has been naturally embedded with the distribution knowledge regarding natural images, and thus spontaneously attains the underlying potential for strong image denoising.
1 code implementation • 7 Dec 2023 • Zhixiang Wei, Lin Chen, Yi Jin, Xiaoxiao Ma, Tianle Liu, Pengyang Ling, Ben Wang, Huaian Chen, Jinjin Zheng
Driven by the motivation that Leveraging Stronger pre-trained models and Fewer trainable parameters for Superior generalizability, we introduce a robust fine-tuning approach, namely Rein, to parameter-efficiently harness VFMs for DGSS.
no code implementations • 12 Sep 2023 • Xiaoxiao Ma, Chunzhi Yi, Zhicai Zhong, Hui Zhou, Baichun Wei, Haiqi Zhu, Feng Jiang
An essential premise for neuroscience brain network analysis is the successful segmentation of the cerebral cortex into functionally homogeneous regions.
1 code implementation • 18 Oct 2022 • Fanzhen Liu, Xiaoxiao Ma, Jia Wu, Jian Yang, Shan Xue, Amin Beheshti, Chuan Zhou, Hao Peng, Quan Z. Sheng, Charu C. Aggarwal
To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes.
1 code implementation • 14 Jun 2021 • Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z. Sheng, Hui Xiong, Leman Akoglu
In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection.
1 code implementation • 5 Oct 2018 • Perttu Hämäläinen, Amin Babadi, Xiaoxiao Ma, Jaakko Lehtinen
Proximal Policy Optimization (PPO) is a highly popular model-free reinforcement learning (RL) approach.