针对网页级时序与视觉分析中的优化难题，提出轻量级Transformer与新型优化器。
EEO-TFV: Escape-Explore Optimizer for Web-Scale Time-Series Forecasting and Vision Analysis
- 设计轻量Transformer结合新优化器，增强探索能力与泛化性。
- 在11个时序数据集和医学图像分割任务中达到领先性能。
- 适合需要高稳定性和跨任务泛化的网页大数据分析场景。
基于Transformer的基座模型在时间序列预测和图像分割等任务中取得显著进展，但在多变量长序列预测中常出现误差累积，在图像相关任务中对分布外样本敏感。这些问题在涉及复杂时序模式和多模态特征的网页级数据分析中尤为突出，导致优化难度剧增，模型易陷入高维参数空间的鞍点停滞。为此，本文提出一种轻量级Transformer架构与新型逃逸-探索优化器（EEO）。该优化器有效避免尖锐极小值与鞍点陷阱，同时提升探索与泛化能力。实验表明，在代表性网页数据场景下，本方法在11个时序基准数据集及Synapse医学图像分割任务上性能媲美当前最优模型，且展现出更优的泛化性与稳定性，验证其作为跨任务网页级数据挖掘与分析基座模型的潜力。
原文摘要 · Abstract (English)
Transformer-based foundation models have achieved remarkable progress in tasks such as time-series forecasting and image segmentation. However, they frequently suffer from error accumulation in multivariate long-sequence prediction and exhibit vulnerability to out-of-distribution samples in image-related tasks. Furthermore, these challenges become particularly pronounced in large-scale Web data analysis tasks, which typically involve complex temporal patterns and multimodal features. This complexity substantially increases optimization difficulty, rendering models prone to stagnation at saddle points within high-dimensional parameter spaces. To address these issues, we propose a lightweight Transformer architecture in conjunction with a novel Escape-Explore Optimizer (EEO). The optimizer enhances both exploration and generalization while effectively avoiding sharp minima and saddle-point traps. Experimental results show that, in representative Web data scenarios, our method achieves performance on par with state-of-the-art models across 11 time-series benchmark datasets and the Synapse medical image segmentation task. Moreover, it demonstrates superior generalization and stability, thereby validating its potential as a versatile cross-task foundation model for Web-scale data mining and analysis.
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