Automated Siamese Network Design for Image Similarity Computation

Despite the success of Siamese networks in image indexing, face recognition, and signature verification, there has been little research on designing their architectural space compared to convolutional neural networks (CNNs). This work aims to automate the design process of Siamese network architectures and improve their performance in tasks that involve image similarity computing such as indexing and retrieval. To achieve this goal, in contrast with the current literature that focuses on improving the design of the backbone CNN, we use Differentiable Neural Architecture Search (DNAS) to explore the architecture of the Multi-Layer Perceptron (MLP) component of siamese networks, namely the projector and/or predictor heads. The main objective of these MLPs is to enhance the ability of backbone CNNs to learn strong representations from unlabeled data. Using a well-known contrastive learning framework (SimCLR) as a baseline, we show that our approach managed to improve performance on several computer vision tasks such as image classification (ImageNet) and content-based image retrieval (INRIA Holidays).