摘要:深度学习算法在很多有监督学习任务上达到了令人满意的结果，但其依赖于大量标注样本，并且使用特定类别训练的分类器，只能对这些类别进行分类.零次学习希望计算机像人类一样，能够结合历史经验与知识进行推理，无需使用大量新类别样本训练，便可达到识别新类别的效果.发现了零次学习任务存在“冷启动”以及矩阵稀疏两个特点，这些特点在推荐任务中同样存在.受推荐任务启发，将零次图像分类任务建模为矩阵填充问题，借鉴推荐领域中协同过滤算法，将稀疏的样本标签矩阵视为非稀疏的视觉特征矩阵和类别特征矩阵的内积结果，进而实现对新类别样本的分类预测.此外，构建了基于类间语义关联的语义图结构，使用图神经网络进行已知类别和新类别之间的知识迁移，以较小代价为类别学得准确的语义特征.在3个经典零次学习数据集上分别进行传统零次学习和广义零次学习实验，实验结果表明：提出的协同过滤式零次学习方法能够有效提升分类精度，且训练代价较小.



Abstract:Many deep learning algorithms have achieved satisfactory results on many supervised learning tasks, but they rely on a large number of labeled samples, and the classifiers trained with specific categories can only classify these categories. Zero-shot learning wishes that the computer can reason like a human, it uses historical knowledge to infer the characteristics of new objects and has the ability to recognize novel categories without lots of samples. It is found that there are sparse matrix and "cold-start" phenomena in zero-shot learning task, these phenomena are also in the recommendation tasks. Inspired by the recommendation tasks, the zero-shot classification task is modeled as a matrix completion problem, hoping to learn from the collaborative filtering algorithms in the recommendation field, which regards the sparse labeled matrix as the product of the visual feature matrix and semantic feature matrix, and then classifies the novel samples. In order to make the semantic representation of each category more accurate, a semantic graph structure is constructed based on the semantic relations between categories and a graph neural network is applied on it for information transferring between known and novel categories. Traditional zero-shot learning and generalized zero-shot learning experiments are performed on three classic zero-shot learning data sets. The experimental results show that the collaborative filtering based zero-shot learning method proposed in this study can effectively improve the classification accuracy, and the training cost is relatively small.



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