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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Liang Hou Qi Cao Yige Yuan Songtao Zhao Chongyang Ma Siyuan Pan Pengfei Wan Zhongyuan Wang Huawei Shen Xueqi Cheng CAS Key Laboratory of AI Safety and Security Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences and Kuaishou Technology CAS Key Laboratory of AI Safety and Security Institute of Computing Technology Chinese Academy of Sciences CAS Key Laboratory of AI Safety and Security Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences Kuaishou Technology CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences

Training generative adversarial networks (GANs) with limited data is challenging because the discriminator is prone to overfitting. Previously proposed differentiable augmentation demonstrates improved data efficiency of training GANs. However, the augmentation implicitly introduces undesired invariance to augmentation for the discriminator since it ignores the change of semantics in the label space caused by data transformation, which may limit the representation learning ability of the discriminator and ultimately affect the generative modeling performance of the generator. To mitigate the negative impact of invariance while inheriting the benefits of data augmentation, we propose a novel augmentation-aware self-supervised discriminator that predicts the augmentation parameter of the augmented data. Particularly, the prediction targets of real data and generated data are required to be distinguished since they are different during training. We further encourage the generator to adversarially learn from the self-supervised discriminator by generating augmentation-predictable real and not fake data. This formulation connects the learning objective of the generator and the arithmetic - harmonic mean divergence under certain assumptions. We compare our method with state-of-the-art (SOTA) methods using the class-conditional BigGAN and unconditional StyleGAN2 architectures on data-limited CIFAR-10, CIFAR-100, FFHQ, LSUN-Cat, and five low-shot datasets. Experimental results demonstrate significant improvements of our method over SOTA methods in training data-efficient GANs. Our code is available at https://***/liang-hou/augself-gan.

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MIGE: A Unified Framework for Multimodal Instruction-Based Image Generation and Editing

arXiv

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arXiv 2025年

作者： Tian, Xueyun Li, Wei Xu, Bingbing Yuan, Yige Wang, Yuanzhuo Shen, Huawei CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Despite significant progress in diffusion-based image generation, subject-driven generation and instruction-based editing remain challenging. Existing methods typically treat them separately, struggling with limited high-quality data and poor generalization. However, both tasks require capturing complex visual variations while maintaining consistency between inputs and outputs. Therefore, we propose MIGE, a unified framework that standardizes task representations using multimodal instructions. It treats subject-driven generation as creation on a blank canvas and instruction-based editing as modification of an existing image, establishing a shared input-output formulation. MIGE introduces a novel multimodal encoder that maps free-form multimodal instructions into a unified vision-language space, integrating visual and semantic features through a feature fusion mechanism. This unification enables joint training of both tasks, providing two key advantages: (1) Cross-Task Enhancement: By leveraging shared visual and semantic representations, joint training improves instruction adherence and visual consistency in both subject-driven generation and instruction-based editing. (2) Generalization: Learning in a unified format facilitates cross-task knowledge transfer, enabling MIGE to generalize to novel compositional tasks, including instruction-based subject-driven editing. Experiments show that MIGE excels in both subject-driven generation and instruction-based editing while setting a state-of-the-art in the new task of instruction-based subject-driven editing. Code and model have been publicly available at this link. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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CTRLORA: AN EXTENSIBLE AND EFFICIENT FRAMEWORK FOR CONTROLLABLE IMAGE GENERATION

arXiv

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arXiv 2024年

作者： Xu, Yifeng He, Zhenliang Shan, Shiguang Chen, Xilin Key Lab of AI Safety Institute of Computing Technology CAS China University of Chinese Academy of Sciences China

Recently, large-scale diffusion models have made impressive progress in text-to-image (T2I) generation. To further equip these T2I models with fine-grained spatial control, approaches like ControlNet introduce an extra network that learns to follow a condition image. However, for every single condition type, ControlNet requires independent training on millions of data pairs with hundreds of GPU hours, which is quite expensive and makes it challenging for ordinary users to explore and develop new types of conditions. To address this problem, we propose the CtrLoRA framework, which trains a Base ControlNet to learn the common knowledge of image-to-image generation from multiple base conditions, along with condition-specific LoRAs to capture distinct characteristics of each condition. Utilizing our pretrained Base ControlNet, users can easily adapt it to new conditions, requiring as few as 1,000 data pairs and less than one hour of single-GPU training to obtain satisfactory results in most scenarios. Moreover, our CtrLoRA reduces the learnable parameters by 90% compared to ControlNet, significantly lowering the threshold to distribute and deploy the model weights. Extensive experiments on various types of conditions demonstrate the efficiency and effectiveness of our method. Codes and model weights will be released at https://***/xyfJASON/ctrlora. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

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Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation

arXiv

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arXiv 2024年

作者： Zhang, Kaike Cao, Qi Wu, Yunfan Sun, Fei Shen, Huawei Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China

Adversarial Collaborative Filtering (ACF), which typically applies adversarial perturbations at user and item embeddings through adversarial training, is widely recognized as an effective strategy for enhancing the robustness of Collaborative Filtering (CF) recommender systems against poisoning attacks. Besides, numerous studies have empirically shown that ACF can also improve recommendation performance compared to traditional CF. Despite these empirical successes, the theoretical understanding of ACF’s effectiveness in terms of both performance and robustness remains unclear. To bridge this gap, in this paper, we first theoretically show that ACF can achieve a lower recommendation error compared to traditional CF with the same training epochs in both clean and poisoned data contexts. Furthermore, by establishing bounds for reductions in recommendation error during ACF’s optimization process, we find that applying personalized magnitudes of perturbation for different users based on their embedding scales can further improve ACF’s effectiveness. Building on these theoretical understandings, we propose Personalized Magnitude Adversarial Collaborative Filtering (PamaCF). Extensive experiments demonstrate that PamaCF effectively defends against various types of poisoning attacks while significantly enhancing recommendation performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender Systems

arXiv

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arXiv 2024年

作者： Wu, Yunfan Cao, Qi Tao, Shuchang Zhang, Kaike Sun, Fei Shen, Huawei Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing China

Recent studies have demonstrated the vulnerability of recommender systems to data poisoning attacks, where adversaries inject carefully crafted fake user interactions into the training data of recommenders to promote target items. Current attack methods involve iteratively retraining a surrogate recommender on the poisoned data with the latest fake users to optimize the attack. However, this repetitive retraining is highly time-consuming, hindering the efficient assessment and optimization of fake users. To mitigate this computational bottleneck and develop a more effective attack in an affordable time, we analyze the retraining process and find that a change in the representation of one user/item will cause a cascading effect through the user-item interaction graph. Under theoretical guidance, we introduce Gradient Passing (GP), a novel technique that explicitly passes gradients between interacted user-item pairs during backpropagation, thereby approximating the cascading effect and accelerating retraining. With just a single update, GP can achieve effects comparable to multiple original training iterations. Under the same number of retraining epochs, GP enables a closer approximation of the surrogate recommender to the victim. This more accurate approximation provides better guidance for optimizing fake users, ultimately leading to enhanced data poisoning attacks. Extensive experiments on real-world datasets demonstrate the efficiency and effectiveness of our proposed GP. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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IS FACTUALITY ENHANCEMENT A FREE LUNCH FOR LLMS? BETTER FACTUALITY CAN LEAD TO WORSE CONTEXT-FaiTHFULNESS

arXiv

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arXiv 2024年

作者： Bi, Baolong Liu, Shenghua Wang, Yiwei Mei, Lingrui Fang, Junfeng Gao, Hongcheng Ni, Shiyu Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology CAS China University of Chinese Academy of Sciences China University of California Merced United States Hefei China

As the modern tools of choice for text understanding and generation, large language models (LLMs) are expected to accurately output answers by leveraging the input context. This requires LLMs to possess both context-faithfulness and factual accuracy. Extensive efforts have been made to enable better outputs from LLMs by mitigating hallucinations through factuality enhancement methods. However, they also pose risks of hindering context-faithfulness, as factuality enhancement can lead LLMs to become overly confident in their parametric knowledge, causing them to overlook the relevant input context. In this work, we argue that current factuality enhancement methods can significantly undermine the context-faithfulness of LLMs. We first revisit the current factuality enhancement methods and evaluate their effectiveness in enhancing factual accuracy. Next, we evaluate their performance on knowledge editing tasks to assess the potential impact on context-faithfulness. The experimental results reveal that while these methods may yield inconsistent improvements in factual accuracy, they also cause a more severe decline in context-faithfulness, with the largest decrease reaching a striking 69.7%. To explain these declines, we analyze the hidden states and logit distributions for the tokens representing new knowledge and parametric knowledge respectively, highlighting the limitations of current approaches. Our finding highlights the complex trade-offs inherent in enhancing LLMs. Therefore, we recommend that more research on LLMs’ factuality enhancement make efforts to reduce the sacrifice of context-faithfulness. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts?

arXiv

引用

arXiv 2024年

作者： Tan, Hexiang Sun, Fei Yang, Wanli Wang, Yuanzhuo Cao, Qi Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

While auxiliary information has become a key to enhancing Large Language Models (LLMs), relatively little is known about how LLMs merge these contexts, specifically contexts generated by LLMs and those retrieved from external sources. To investigate this, we formulate a systematic framework to identify whether LLMs’ responses are attributed to either generated or retrieved contexts. To easily trace the origin of the response, we construct datasets with conflicting contexts, i.e., each question is paired with both generated and retrieved contexts, yet only one of them contains the correct answer. Our experiments reveal a significant bias in several LLMs (GPT-4/3.5 and Llama2) to favor generated contexts, even when they provide incorrect information. We further identify two key factors contributing to this bias: i) contexts generated by LLMs typically show greater similarity to the questions, increasing their likelihood of being selected;ii) the segmentation process used in retrieved contexts disrupts their completeness, thereby hindering their full utilization in LLMs. Our analysis enhances the understanding of how LLMs merge diverse contexts, offers valuable insights for advancing current LLM augmentation methods, and highlights the risk of generated misinformation for retrieval-augmented LLMs. © 2024, CC0.

关键词： Computational linguistics

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A Review of Information Retrieval in the Era of Large Language Models 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Pang, Liang Deng, Jingcheng Gu, Jia Shen, Huawei Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences / Beijing 100190 University of Chinese Academy of Sciences / Beijing 100190

ISBN: (纸本)9780000000002

The rapid development of generative artificial intelligence, exemplified by large language models, signifies a shift in artificial intelligence from the era of discrimination to generation. This advancement has greatly propelled the development of information retrieval technology. This paper provides an in-depth review of the impact of large language models on information retrieval. It discusses the influence of large language models on information retrieval, from performance improvements to paradigm shifts. In the context of traditional information retrieval processes, large language models significantly enhance the performance of various information retrieval modules such as indexing, retrieval, and ranking, owing to their powerful semantic understanding and modeling capabilities. Additionally, the paper explores the potential trend of large language models replacing traditional information retrieval methods and giving rise to new ways of obtaining information, possibly marking a new Cambrian explosion in the information age. The profound impact of large language models on the content ecosystem is also worthy of attention. © CCL 2024 - 23rd Chinese National Conference on Computational Linguistics. All rights reserved.

关键词： Semantics

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Synthetic View Augmentation for Sign Language Recognition 25

Synthetic View Augmentation for Sign Language Recognition

引用

Companion Proceedings of the ACM on Web Conference 2025

作者： Yuting Peng Peiqi Jiao Honggang Zou Yuecong Min Xilin Chen State Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China State Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400713316

Sign language, as a visual language, is inherently spatial and dynamic, characterized by various hand shapes, movements, facial expressions, and body postures. In real-world scenarios, sign language recognition (SLR) systems are deployed in diverse environments with varying lighting conditions, backgrounds, and user positions, and models trained on data from a single viewpoint often struggle to generalize effectively to new perspectives. To address these limitations, this paper focuses on cross-view SLR and proposes a synthetic view augmentation pipeline to facilitate SLR models learning viewpoint-invariant features during training, thereby improving their performance on unseen viewpoints. Additionally, we introduce the XV-SLR model, which incorporates multi-modality information further to enhance the robustness of cross-view sign language recognition. The proposed method achieves a top-1 accuracy of 56.87% under the RGB setting and 57.97% under the RGB-D setting and outperforms other approaches in the Cross-View Isolated Sign Language Recognition Challenge (CV-ISLR). Code is available at https://***/VIPL-SLP/XV-SLR.

关键词： cross-view recognition

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FCS-HGNN: Flexible Multi-type Community Search in Heterogeneous Information Networks

arXiv

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arXiv 2023年

作者： Chen, Guoxin Guo, Fangda Wang, Yongqing Liu, Yanghao Yu, Peiying Shen, Huawei Cheng, Xueqi Key Laboratory of AI Safety Institute of Computing Technology CAS University of Chinese Academy of Sciences Beijing China Key Laboratory of AI Safety Institute of Computing Technology CAS BeiJing China Natural Language Processing Lab School of Computer Science & Technology Soochow University Suzhou China

Community search is a personalized community discovery problem designed to identify densely connected subgraphs containing the query node. Recently, community search in heterogeneous information networks (HINs) has received considerable attention. Existing methods typically focus on modeling relationships in HINs through predefined meta-paths or user-specified relational constraints. However, metapath-based methods are primarily designed to identify single-type communities with nodes of the same type rather than multi-type communities involving nodes of different types. Constraint-based methods require users to have a good understanding of community patterns to define a suitable set of relational constraints, which increases the burden on users. In this paper, we propose FCS-HGNN, a novel method for flexibly identifying both single-type and multi-type communities in HINs. Specifically, FCS-HGNN extracts complementary information from different views and dynamically considers the contribution of each relation instead of treating them equally, thereby capturing more fine-grained heterogeneous information. Furthermore, to improve efficiency on large-scale graphs, we further propose LS-FCS-HGNN, which incorporates i) the neighbor sampling strategy to improve training efficiency, and ii) the depth-based heuristic search strategy to improve query efficiency. We conducted extensive experiments to demonstrate the superiority of our proposed methods over state-of-the-art methods, achieving average improvements of 14.3% and 11.1% on single-type and multi-type communities, respectively.1 © 2023, CC BY-NC-ND.

关键词： Structured Query Language

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