Li Gao

AAAI Conference 2026 Conference Paper

Unifying Locality of KANs and Feature Drift Compensation Projection for Data-Free Replay Based Continual Face Forgery Detection

• Tianshuo Zhang
• Siran Peng
• Li Gao
• Haoyuan Zhang
• Xiangyu Zhu
• Zhen Lei

The rapid advancements in face forgery techniques necessitate that detectors continuously adapt to new forgery methods, thus situating face forgery detection within a continual learning paradigm. However, when detectors learn new forgery types, their performance on previous types often degrades rapidly, a phenomenon known as catastrophic forgetting. Kolmogorov-Arnold Networks (KANs) utilize locally plastic splines as their activation functions, enabling them to learn new tasks by modifying only local regions of the functions while leaving other areas unaffected. Therefore, they are naturally suitable for addressing catastrophic forgetting. However, KANs have two significant limitations: 1) the splines are ineffective for modeling high-dimensional images, while alternative activation functions that are suitable for images lack the essential property of locality; 2) in continual learning, when features from different domains overlap, the mapping of different domains to distinct curve regions always collapses due to repeated modifications of the same regions. In this paper, we propose a KAN-based Continual Face Forgery Detection (KAN-CFD) framework, which includes a Domain-Group KAN Detector (DG-KD) and a data-free replay Feature Separation strategy via KAN Drift Compensation Projection (FS-KDCP). DG-KD enables KANs to fit high-dimensional image inputs while preserving locality and local plasticity. FS-KDCP avoids the overlap of the KAN input spaces without using data from prior tasks. Experimental results demonstrate that the proposed method achieves superior performance while notably reducing forgetting.

NeurIPS Conference 2025 Conference Paper

DevFD : Developmental Face Forgery Detection by Learning Shared and Orthogonal LoRA Subspaces

• Tianshuo Zhang
• Li Gao
• Siran Peng
• Xiangyu Zhu
• Zhen Lei

The rise of realistic digital face generation and manipulation poses significant social risks. The primary challenge lies in the rapid and diverse evolution of generation techniques, which often outstrip the detection capabilities of existing models. To defend against the ever-evolving new types of forgery, we need to enable our model to quickly adapt to new domains with limited computation and data while avoiding forgetting previously learned forgery types. In this work, we posit that genuine facial samples are abundant and relatively stable in acquisition methods, while forgery faces continuously evolve with the iteration of manipulation techniques. Given the practical infeasibility of exhaustively collecting all forgery variants, we frame face forgery detection as a continual learning problem and allow the model to develop as new forgery types emerge. Specifically, we employ a Developmental Mixture of Experts (MoE) architecture that uses LoRA models as its individual experts. These experts are organized into two groups: a Real-LoRA to learn and refine knowledge of real faces, and multiple Fake-LoRAs to capture incremental information from different forgery types. To prevent catastrophic forgetting, we ensure that the learning direction of Fake-LoRAs is orthogonal to the established subspace. Moreover, we integrate orthogonal gradients into the orthogonal loss of Fake-LoRAs, preventing gradient interference throughout the training process of each task. Experimental results under both the datasets and manipulation types incremental protocols demonstrate the effectiveness of our method.

AAAI Conference 2025 Conference Paper

Towards S²-Challenges Underlying LLM-Based Augmentation for Personalized News Recommendation

• Shicheng Wang
• Hengzhu Tang
• Li Gao
• Shu Guo
• Suqi Cheng
• Junfeng Wang
• Dawei Yin
• Tingwen Liu

Personalized news recommendation aims to recommend candidate news to the target user. Since the data and knowledge involved in traditional recommender systems are restricted, recent studies utilize large language models (LLMs) to generate news articles and augment the original dataset. However, despite the superiority of LLM-based augmentation in news recommendation, previous studies still suffer from two serious problems, i.e., structure-level deficiency and semantic-level noise. Since the LLM-based augmentation is mainly implemented at the semantic level, collaborative signals, the critical structure information in recommender systems, is neglected during the generation process. Thus, it is inappropriate to perform recommendation based on the augmented user-news bipartite, which manifests as multiple isolated cliques. Moreover, utilizing the open-world knowledge of LLMs to extend the closed systems will inevitably introduce noise information, leading to difficulties in mining users' real preferences. In this paper, we propose a novel Structure-aware and Semantic-aware approach for LLM-Empowered personalized News Recommendation, named S^2LENR, to tackle the mentioned problems. Specifically, we propose a structure-aware refinement module to inject collaborative information in a parametric way, in order to construct a valid augmented bipartite. Besides, we devise a semantic-aware denoising module utilizing contrastive learning paradigm to overcome the negative effects of noise information. Finally, we calculate the relevance score between target user and candidate news representations. We conduct experiments on two real-world news recommendation datasets MIND-Large, MIND-Small and empirical results demonstrate the effectiveness of our approach from multiple perspectives.

EAAI Journal 2023 Journal Article

Frequency matching optimization model of ultrasonic scalpel transducer based on neural network and reinforcement learning

• Li Gao
• Sheng-long Yang
• Bin Meng
• Guo-xiang Tong
• Hai-Ping Fan
• Gui-Song Yang

AAAI Conference 2022 Short Paper

MMAN: Metapath Based Multi-Level Graph Attention Networks for Heterogeneous Network Embedding (Student Abstract)

• Jie Liu
• Lingyun Song
• Li Gao
• Xuequn Shang

Current Heterogeneous Network Embedding (HNE) models can be roughly divided into two types, i. e. , relation-aware and metapath-aware models. However, they either fail to represent the non-pairwise relations in heterogeneous graph, or only capable of capturing local information around target node. In this paper, we propose a metapath based multilevel graph attention networks (MMAN) to jointly learn node embeddings on two substructures, i. e. , metapath based graphs and hypergraphs extracted from original heterogeneous graph. Extensive experiments on three benchmark datasets for node classification and node clustering demonstrate the superiority of MMAN over the state-of-the-art works.

AAAI Conference 2021 Conference Paper

Addressing Domain Gap via Content Invariant Representation for Semantic Segmentation

• Li Gao
• Lefei Zhang
• Qian Zhang

The problem of unsupervised domain adaptation in semantic segmentation is a major challenge for numerous computer vision tasks because acquiring pixel-level labels is timeconsuming with expensive human labor. A large gap exists among data distributions in different domains, which will cause severe performance loss when a model trained with synthetic data is generalized to real data. Hence, we propose a novel domain adaptation approach, called Content Invariant Representation Network, to narrow the domain gap between the source (S) and target (T) domains. The previous works developed a network to directly transfer the knowledge from the S to T. On the contrary, the proposed method aims to progressively reduce the gap between S and T on the basis of a Content Invariant Representation (CIR). CIR is an intermediate domain (I) sharing invariant content with S and having similar data distribution to T. Then, an Ancillary Classifier Module (ACM) is designed to focus on pixel-level details and generate attention-aware results. ACM adaptively assigns different weights to pixels according to their domain offsets, thereby reducing local domain gaps. The global domain gap between CIR and T is also narrowed by enforcing local alignments. Last, we perform self-supervised training in the pseudo-labeled target domain to further fit the distribution of the real data. Comprehensive experiments on two domain adaptation tasks, that is, GTAV → Cityscapes and SYNTHIA → Cityscapes, clearly demonstrate the superiority of our method compared with state-of-the-art methods.

EAAI Journal 2020 Journal Article

A user-knowledge crowdsourcing task assignment model and heuristic algorithm for Expert Knowledge Recommendation Systems

• Li Gao
• Yi Gan
• Binghai Zhou
• Mengyu Dong

YNIMG Journal 2019 Journal Article

Agreeableness modulates group member risky decision-making behavior and brain activity

• Fang Wang
• Xin Wang
• Fenghua Wang
• Li Gao
• Hengyi Rao
• Yu Pan

AAAI Conference 2019 Conference Paper

Translating with Bilingual Topic Knowledge for Neural Machine Translation

• Xiangpeng Wei
• Yue Hu
• Luxi Xing
• Yipeng Wang
• Li Gao

The dominant neural machine translation (NMT) models that based on the encoder-decoder architecture have recently achieved the state-of-the-art performance. Traditionally, the NMT models only depend on the representations learned during training for mapping a source sentence into the target domain. However, the learned representations often suffer from implicit and inadequately informed properties. In this paper, we propose a novel bilingual topic enhanced NMT (BLT- NMT) model to improve translation performance by incorporating bilingual topic knowledge into NMT. Specifically, the bilingual topic knowledge is included into the hidden states of both encoder and decoder, as well as the attention mechanism. With this new setting, the proposed BLT-NMT has access to the background knowledge implied in bilingual topics which is beyond the sequential context, and enables the attention mechanism to attend to topic-level attentions for generating accurate target words during translation. Experimental results show that the proposed model consistently outperforms the traditional RNNsearch and the previous topic-informed NMT on Chinese-English and English- German translation tasks. We also introduce the bilingual topic knowledge into the newly emerged Transformer base model on English-German translation and achieve a notable improvement.

IJCAI Conference 2018 Conference Paper

Active Discriminative Network Representation Learning

• Li Gao
• Hong Yang
• Chuan Zhou
• Jia Wu
• Shirui Pan
• Yue Hu

Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network analysis tasks, such as node classification. It is worth noting that label information is valuable for learning the discriminative network representations. However, labels of all training nodes are always difficult or expensive to obtain and manually labeling all nodes for training is inapplicable. Different sets of labeled nodes for model learning lead to different network representation results. In this paper, we propose a novel method, termed as ANRMAB, to learn the active discriminative network representations with a multi-armed bandit mechanism in active learning setting. Specifically, based on the networking data and the learned network representations, we design three active learning query strategies. By deriving an effective reward scheme that is closely related to the estimated performance measure of interest, ANRMAB uses a multi-armed bandit mechanism for adaptive decision making to select the most informative nodes for labeling. The updated labeled nodes are then used for further discriminative network representation learning. Experiments are conducted on three public data sets to verify the effectiveness of ANRMAB.

IJCAI Conference 2018 Conference Paper

Recommendation with Multi-Source Heterogeneous Information

• Li Gao
• Hong Yang
• Jia Wu
• Chuan Zhou
• Weixue Lu
• Yue Hu

Network embedding has been recently used in social network recommendations by embedding low-dimensional representations of network items for recommendation. However, existing item recommendation models in social networks suffer from two limitations. First, these models partially use item information and mostly ignore important contextual information in social networks such as textual content and social tag information. Second, network embedding and item recommendations are learned in two independent steps without any interaction. To this end, we in this paper consider item recommendations based on heterogeneous information sources. Specifically, we combine item structure, textual content and tag information for recommendation. To model the multi-source heterogeneous information, we use two coupled neural networks to capture the deep network representations of items, based on which a new recommendation model Collaborative multi-source Deep Network Embedding (CDNE for short) is proposed to learn different latent representations. Experimental results on two real-world data sets demonstrate that CDNE can use network representation learning to boost the recommendation performance.

AAAI Conference 2017 Conference Paper

Collaborative Dynamic Sparse Topic Regression with User Profile Evolution for Item Recommendation

• Li Gao
• Jia Wu
• Chuan Zhou
• Yue Hu

In many time-aware item recommender systems, modeling the accurate evolution of both user profiles and the contents of items over time is essential. However, most existing methods focus on learning users’ dynamic interests, where the contents of items are assumed to be stable over time. They thus fail to capture the dynamic changes in the item’s contents. In this paper, we present a novel method CDUE for time-aware item recommendation, which captures the evolution of both user’s interests and item’s contents information via topic dynamics. Specifically, we propose a dynamic sparse topic model to track the evolution of topics for changes in items’ contents over time and adapt a vector autoregressive model to profile users’ dynamic interests. The item’s topics and user’s interests and their evolutions are learned collaboratively and simultaneously into a unified learning framework. Experimental results on two real-world data sets demonstrate the quality and effectiveness of the proposed method and show that our method can be used to make better future recommendations.

IJCAI Conference 2016 Conference Paper

Semi-Data-Driven Network Coarsening

• Li Gao
• Jia Wu
• Hong Yang
• Zhi Qiao
• Chuan Zhou
• Yue Hu

Network coarsening refers to a new class of graph `zoom-out' operations by grouping similar nodes and edges together so that a smaller equivalent representation of the graph can be obtained for big network analysis. Existing network coarsening methods consider that network structures are static and thus cannot handle dynamic networks. On the other hand, data-driven approaches can infer dynamic network structures by using network information spreading data. However, existing data-driven approaches neglect static network structures that are potentially useful for inferring big networks. In this paper, we present a new semi-data-driven network coarsening model to learn coarsened networks by embedding both static network structure data and dynamic network information spreading data. We prove that the learning model is convex and the Accelerated Proximal Gradient algorithm is adapted to achieve the global optima. Experiments on both synthetic and real-world data sets demonstrate the quality and effectiveness of the proposed method.

IJCAI Conference 2015 Conference Paper

Influence Maximization in Big Networks: An Incremental Algorithm for Streaming Subgraph Influence Spread Estimation

• Wei-Xue Lu
• Peng Zhang
• Chuan Zhou
• Chunyi Liu
• Li Gao

Influence maximization plays a key role in social network viral marketing. Although the problem has been widely studied, it is still challenging to estimate influence spread in big networks with hundreds of millions of nodes. Existing heuristic algorithms and greedy algorithms incur heavy computation cost in big networks and are incapable of processing dynamic network structures. In this paper, we propose an incremental algorithm for influence spread estimation in big networks. The incremental algorithm breaks down big networks into small subgraphs and continuously estimate influence spread on these subgraphs as data streams. The challenge of the incremental algorithm is that subgraphs derived from a big network are not independent and MC simulations on each subgraph (defined as snapshots) may conflict with each other. In this paper, we assume that different combinations of MC simulations on subgraphs generate independent samples. In so doing, the incremental algorithm on streaming subgraphs can estimate influence spread with fewer simulations. Experimental results demonstrate the performance of the proposed algorithm.