> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/deep-learning/siamese-nets.md). # Siamese Nets When there are only one or a few examples per class, a network cannot learn a flat class label, so it learns a similarity between inputs instead. The page follows that idea from the siamese setup and its losses to one-shot learning and then to multi networks. The same notes are in [N-Shot Learning](/problem-framing/n-shot-learning.md) and [SIAMESE NETWORKS](/deep-learning/deep-neural-nets.md#siamese-networks). The starting point is a Siamese CNN that learns a similarity between images, not to classify; the face-recognition-from-scratch source for that note no longer opens and is kept at the end of the page. The mechanism is explained in [Visual tracking, explains contrastive and triplet loss](https://medium.com/intel-student-ambassadors/siamese-networks-for-visual-tracking-96262eaaba77), Param Popat's post on siamese networks for visual tracking: two or more identical networks share weights and work on two different input vectors to compute comparable output vectors. With the loss in hand, the author's next notes were One shot learning, very thorough, baseline vs siamese, and What is triplet loss; both sources are kept at the end of the page. The same sharing idea extends to MULTI NETWORKS. [Google whitening black boxes using multi nets, segmentation and classification](https://medium.com/health-ai/google-deepmind-might-have-just-solved-the-black-box-problem-in-medical-ai-3ed8bc21f636) is Susan Ruyu Qi on Google DeepMind improving the interpretability of the “Black Box” in medical imaging, where it is hard to understand why a model makes a certain diagnosis or recommendation. ## Deprecated links {% hint style="warning" %} These links and images no longer work. The original wording is kept here. A same-resource copy, when one was checked, is used above. {% endhint %} \- Towards Data Science: one-shot-learning-with-siamese-networks-using-keras-17f34e75bb3d. This address no longer opens: - Towards Data Science: siamese-network-triplet-loss-b4ca82c1aec8. This address no longer opens: - Siamese CNN, learns a similarity between images, not to classify. This address no longer opens: --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/deep-learning/siamese-nets.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.