Research
Implementation details of Backpropagation in Siamese networks. [D]
This Reddit discussion on r/MachineLearning explores the practical implementation details of backpropagation in Siamese networks, which consist of two or more identical subnetworks with shared weights
This Reddit discussion on r/MachineLearning explores the practical implementation details of backpropagation in Siamese networks, which consist of two or more identical subnetworks with shared weights that are trained using distance-based loss functions such as contrastive loss or triplet loss. A key implementation consideration is how gradients flow through the shared-weight architecture — because both branches share parameters, gradients from both forward passes are accumulated and summed before the weight update, ensuring the twin subnetworks remain identical. The discussion likely also addresses trade-offs between conventional two-branch input strategies versus more efficient single-branch batching approaches, where all pairwise losses are computed over a mini-batch to maximize the number of effective training pairs.
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Source: r/MachineLearning | 2026-04-13