Core Insights - A research team from the Institute of Software, Chinese Academy of Sciences, proposed a small-batch data sampling strategy to eliminate the interference of unobservable variable semantics on representation learning, enhancing the out-of-distribution generalization ability of self-supervised learning models [1][2] Group 1: Research Findings - The out-of-distribution generalization ability refers to the model's performance on test data that differs from the training data distribution, which is crucial for maintaining effectiveness in unseen data scenarios [1] - The study identified that self-supervised learning models are affected by unobservable variable semantics during training, which weakens their out-of-distribution generalization ability [1] Group 2: Methodology - The proposed strategy utilizes causal effect estimation techniques to eliminate the confounding effects of unobservable variable semantics [1] - By learning a latent variable model, the strategy estimates the posterior probability distribution of unobservable semantic variables given "anchor" samples, termed as balance scores [1] - Samples with similar or close balance scores are grouped into the same small-batch dataset, ensuring that unobservable semantic variables are conditionally independent of the "anchor" samples within each batch [1] Group 3: Experimental Results - Extensive experiments on benchmark datasets showed that the sampling strategy improved the performance of mainstream self-supervised learning methods by at least 2% across various evaluation tasks [2] - In classification tasks on ImageNet100 and ImageNet, both Top-1 and Top-5 accuracy surpassed the state-of-the-art self-supervised methods [2] - In semi-supervised classification tasks, Top-1 and Top-5 accuracy increased by over 3% and 2%, respectively [2] - The strategy also provided stable gains in average precision for object detection and instance segmentation transfer learning tasks [2] - Performance improvements exceeded 5% for few-shot transfer learning tasks on datasets like Omniglot, miniImageNet, and CIFARFS [2] - The research findings were accepted by the top-tier academic conference in artificial intelligence, International Conference on Machine Learning (ICML-25) [2]

金融工程 | 专题报告 DFQ-diversify：解决分布外泛化问题的自 监督领域识别与对抗解耦模型 ——因子选股系列之一一五 研究结论 DFQ-Diversify 模型有效解决分布外泛化问题 ⚫ 本文提出全新模型 DFQ-Diversify，通过引入自监督领域识别与对抗训练机制，实现 标签预测任务与领域识别任务的显式解耦。该模型无需人工预设环境变量，能够自 主识别潜在领域信息，进而提取出对外部扰动不敏感、跨领域稳定的预测特征，增 强模型的分布外泛化能力。 模型创新性地引入"领域-标签"解耦框架 ⚫ 模型训练流程包含三个核心模块：update_d、set_dlabel 和 update，通过对抗训练 机制同时完成领域识别与标签预测任务，实现两者的显式解耦。 自监督动态领域划分机制提升灵活性与泛化适应能力 三重对抗训练机制增强特征解耦与迁移稳健性 与 Factorvae-pro 的对比：从静态环境变量到动态领域建模 多市场回测表现优异，泛用性强 ⚫ 模型在中证全指、沪深 300、中证 500 等多个股票池中均取得显著绩效，尤其在大 盘股表现突出。2020-2025 年间，中证全指池中 IC 达 12.22%， ...