Meng Wang

Mingyu Xing, Lechao Cheng, Shengeng Tang et al.

We introduce \textbf{Knowledge Swapping}, a novel task designed to selectively regulate knowledge of a pretrained model by enabling the forgetting of user\-specified information, retaining essential knowledge, and acquiring new knowledge simultaneously. By delving into the analysis of knock-on feature hierarchy, we find that incremental learning typically progresses from low\-level representations to higher\-level semantics, whereas forgetting tends to occur in the opposite direction\-starting from high-level semantics and moving down to low-level features. Building upon this, we propose to benchmark the knowledge swapping task with the strategy of \textit{Learning Before Forgetting}. Comprehensive experiments on various tasks like image classification, object detection, and semantic segmentation validate the effectiveness of the proposed strategy. The source code is available at \href{https://github.com/xingmingyu123456/KnowledgeSwapping}{https://github.com/xingmingyu123456/KnowledgeSwapping}.

Lechao Cheng, Kaifeng Chen, Jiyang Li et al.

Learning from noisy data has become essential for adapting deep learning models to real-world applications. Traditional methods often involve first evaluating the noise and then applying strategies such as discarding noisy samples, re-weighting, or re-labeling. However, these methods can fall into a vicious cycle when the initial noise evaluation is inaccurate, leading to suboptimal performance. To address this, we propose a novel approach that leverages dataset distillation for noise removal. This method avoids the feedback loop common in existing techniques and enhances training efficiency, while also providing strong privacy protection through offline processing. We rigorously evaluate three representative dataset distillation methods (DATM, DANCE, and RCIG) under various noise conditions, including symmetric noise, asymmetric noise, and real-world natural noise. Our empirical findings reveal that dataset distillation effectively serves as a denoising tool in random noise scenarios but may struggle with structured asymmetric noise patterns, which can be absorbed into the distilled samples. Additionally, clean but challenging samples, such as those from tail classes in imbalanced datasets, may undergo lossy compression during distillation. Despite these challenges, our results highlight that dataset distillation holds significant promise for robust model training, especially in high-privacy environments where noise is prevalent. The source code is available at https://github.com/Kciiiman/DD_LNL.

Jiawei Chen, Hande Dong, Xiang Wang et al.

While recent years have witnessed a rapid growth of research papers on recommender system (RS), most of the papers focus on inventing machine learning models to better fit user behavior data. However, user behavior data is observational rather than experimental. This makes various biases widely exist in the data, including but not limited to selection bias, position bias, exposure bias, and popularity bias. Blindly fitting the data without considering the inherent biases will result in many serious issues, e.g., the discrepancy between offline evaluation and online metrics, hurting user satisfaction and trust on the recommendation service, etc. To transform the large volume of research models into practical improvements, it is highly urgent to explore the impacts of the biases and perform debiasing when necessary. When reviewing the papers that consider biases in RS, we find that, to our surprise, the studies are rather fragmented and lack a systematic organization. The terminology ``bias'' is widely used in the literature, but its definition is usually vague and even inconsistent across papers. This motivates us to provide a systematic survey of existing work on RS biases. In this paper, we first summarize seven types of biases in recommendation, along with their definitions and characteristics. We then provide a taxonomy to position and organize the existing work on recommendation debiasing. Finally, we identify some open challenges and envision some future directions, with the hope of inspiring more research work on this important yet less investigated topic. The summary of debiasing methods reviewed in this survey can be found at \url{https://github.com/jiawei-chen/RecDebiasing}.