KAN4TSF: Are KAN and KAN-based models Effective for Time Series Forecasting?

Time series forecasting is a crucial task that predicts the future values of variables based on historical data. Time series forecasting techniques have been developing in parallel with the machine learning community, from early statistical learning methods to current deep learning methods. Although existing methods have made significant progress, they still suffer from two challenges. The mathematical theory of mainstream deep learning-based methods does not establish a clear relation between network sizes and fitting capabilities, and these methods often lack interpretability. To this end, we introduce the Kolmogorov-Arnold Network (KAN) into time series forecasting research, which has better mathematical properties and interpretability. First, we propose the Reversible Mixture of KAN experts (RMoK) model, which is a KAN-based model for time series forecasting. RMoK uses a mixture-of-experts structure to assign variables to KAN experts. Then, we compare performance, integration, and speed between RMoK and various baselines on real-world datasets, and the experimental results show that RMoK achieves the best performance in most cases. And we find the relationship between temporal feature weights and data periodicity through visualization, which roughly explains RMoK's mechanism. Thus, we conclude that KAN and KAN-based models (RMoK) are effective in time series forecasting. Code is available at KAN4TSF: https://github.com/2448845600/KAN4TSF.

CoIE: Chain-of-Instruct Editing for Multi-Attribute Face Manipulation

Current text-to-image editing models often encounter challenges with smoothly manipulating multiple attributes using a single instruction. Taking inspiration from the Chain-of-Thought prompting technique utilized in language models, we present an innovative concept known as Chain-of-Instruct Editing (CoIE), which enhances the capabilities of these models through step-by-step editing using a series of instructions. In particular, in the context of face manipulation, we leverage the contextual learning abilities of a pretrained Large Language Model (LLM), such as GPT-4, to generate a sequence of instructions from the original input, utilizing a purpose-designed 1-shot template. To further improve the precision of each editing step, we conduct fine-tuning on the editing models using our self-constructed instruction-guided face editing dataset, Instruct-CelebA. And additionally, we incorporate a super-resolution module to mitigate the adverse effects of editability and quality degradation. Experimental results across various challenging cases confirm the significant boost in multi-attribute facial image manipulation using chain-of-instruct editing. This is evident in enhanced editing success rates, measured by CLIPSim and Coverage metrics, improved by 17.86% and 85.45% respectively, and heightened controllability indicated by Preserve L1 and Quality metrics, improved by 11.58% and 4.93% respectively.

Improved Face Representation via Joint Label Classification and Supervised Contrastive Clustering

Face clustering tasks can learn hierarchical semantic information from large-scale data, which has the potential to help facilitate face recognition. However, there are few works on this problem. This paper explores it by proposing a joint optimization task of label classification and supervised contrastive clustering to introduce the cluster knowledge to the traditional face recognition task in two ways. We first extend ArcFace with a cluster-guided angular margin to adjust the within-class feature distribution according to the hard level of face clustering. Secondly, we propose a supervised contrastive clustering approach to pull the features to the cluster center and propose the cluster-aligning procedure to align the cluster center and the learnable class center in the classifier for joint training. Finally, extensive qualitative and quantitative experiments on popular facial benchmarks demonstrate the effectiveness of our paradigm and its superiority over the existing approaches to face recognition.