In many machine learning applications and algorithms, the algorithm performance and accuracy are highly dependent on the metric used to measure the distance between different samples. Therefore, learning a distance metric specific to the data can improve these algorithms’ performance. This paper proposes an unrestricted deep metric learning framework based on neural networks’ interaction for learning metrics in latent space. The proposed method is inspired by generative neural nets (GANs), in which two neural nets are working together to learn true data distribution. In our method, one network plays the role of a supervisor for another network, a feature learning auto-encoder. Its task is to learn transformation to latent space in which data have more meaningful distance and separability. i.e., the supervisor gets the output of the auto-encoder and sends feedback to modify its weights. They interact with each other interleavingly. Several experiments were conducted on four datasets, such as MNIST, GISETTE, Winnipeg Cropland Classification (WCC), and swarm behavior, from different application domains, to evaluate the proposed method’s performance. The results show that we can force auto-encoder to learn label information to project data into a latent space with better separability by using our approach. In addition to better class discrimination, the proposed method is far faster than normal auto-encoders during feature learning and has much less training time in the classification phase.

在许多机器学习应用程序和算法中，算法性能和准确性高度依赖于用于测量不同样本之间距离的度量标准。因此，学习特定于数据的距离度量可以提高这些算法的性能。本文提出了一种基于神经网络交互的无限制深度度量学习框架，用于在潜在空间中学习度量。所提出的方法受到生成神经网络 (GAN) 的启发，其中两个神经网络协同工作以学习真实的数据分布。在我们的方法中，一个网络充当另一个网络的监督者，即特征学习自动编码器。它的任务是学习到潜在空间的转换，其中数据具有更有意义的距离和可分离性。IE，监督者获取自动编码器的输出并发送反馈以修改其权重。它们相互交错地相互作用。对来自不同应用领域的四个数据集（例如 MNIST、GISETTE、温尼伯农田分类 (WCC) 和群体行为）进行了多次实验，以评估所提出方法的性能。结果表明，通过使用我们的方法，我们可以强制自动编码器学习标签信息以将数据投影到具有更好可分离性的潜在空间中。除了更好的类别区分外，所提出的方法在特征学习期间比普通自动编码器快得多，并且在分类阶段的训练时间要少得多。GISETTE、温尼伯农田分类 (WCC) 和来自不同应用领域的群体行为来评估所提出方法的性能。结果表明，通过使用我们的方法，我们可以强制自动编码器学习标签信息以将数据投影到具有更好可分离性的潜在空间中。除了更好的类别区分外，所提出的方法在特征学习期间比普通自动编码器快得多，并且在分类阶段的训练时间要少得多。GISETTE、温尼伯农田分类 (WCC) 和来自不同应用领域的群体行为来评估所提出方法的性能。结果表明，通过使用我们的方法，我们可以强制自动编码器学习标签信息以将数据投影到具有更好可分离性的潜在空间中。除了更好的类别区分外，所提出的方法在特征学习期间比普通自动编码器快得多，并且在分类阶段的训练时间要少得多。