论文标题
对抗性Lagrangian集成对比度嵌入有限尺寸数据集
Adversarial Lagrangian Integrated Contrastive Embedding for Limited Size Datasets
论文作者
论文摘要
某些数据集包含有限数量的具有高度各种样式和复杂结构的样品。这项研究提出了一种新型的对抗性拉格朗日综合对比度嵌入（ALICE），用于小型数据集。首先，提出的预训练的对抗转移的准确性提高和训练收敛在数据集的各种数据集上显示，样品很少。其次，研究了一种使用各种增强技术的新型对抗综合对比模型。所提出的结构考虑了具有不同外观的输入样品，并通过对抗性对比训练产生了较高的表示。最后，多目标增强的拉格朗日乘数鼓励所呈现的对抗性对比度嵌入的低级别和稀疏性，以自动估计正规化器的系数自动估算到最佳重量。稀疏性约束会抑制特征空间中较少的代表性元素。低级约束消除了微不足道和冗余的组件，并实现了卓越的概括。通过使用基准数据集进行具有小数据样本的场景，通过进行消融研究来验证所提出的模型的性能。
Certain datasets contain a limited number of samples with highly various styles and complex structures. This study presents a novel adversarial Lagrangian integrated contrastive embedding (ALICE) method for small-sized datasets. First, the accuracy improvement and training convergence of the proposed pre-trained adversarial transfer are shown on various subsets of datasets with few samples. Second, a novel adversarial integrated contrastive model using various augmentation techniques is investigated. The proposed structure considers the input samples with different appearances and generates a superior representation with adversarial transfer contrastive training. Finally, multi-objective augmented Lagrangian multipliers encourage the low-rank and sparsity of the presented adversarial contrastive embedding to adaptively estimate the coefficients of the regularizers automatically to the optimum weights. The sparsity constraint suppresses less representative elements in the feature space. The low-rank constraint eliminates trivial and redundant components and enables superior generalization. The performance of the proposed model is verified by conducting ablation studies by using benchmark datasets for scenarios with small data samples.
