{
  "annotation_id": "a35978a1-7378-4b08-9ab9-6b2b8272c934",
  "date_created": "2026-02-17T05:53:15.960000Z",
  "date_modified": "2026-02-17T05:53:15.960000Z",
  "file_hash": "316d81a9ca746dbb9c2e70834b4d5b09f2c66d91d415a43674f9cde64e45e428",
  "private": false,
  "record": {
    "abstract": "Convolutional neural networks (CNNs) have been widely used in the computer vision community, significantly improving the state-of-the-art. But learning good features often is computationally expensive in machine learning settings and is especially difficult when there is a lack of data. One-shot learning is one such area where only limited data is available. In one-shot learning, predictions have to be made after seeing only one example from one class, which requires special techniques. In this paper we explore different approaches to one-shot identification tasks in different domains including an industrial application and face recognition. We use a special technique with stacked images and use siamese capsule networks. It is encouraging to see that the approach using capsule architecture achieves strong results and exceeds other techniques on a wide range of datasets from industrial application to face recognition benchmarks while being easy to use and optimise.",
    "arxiv_id": "2601.08278",
    "authors": [
      "Janis Mohr",
      "J\u00f6rg Frochte"
    ],
    "categories": [
      "cs.CV",
      "cs.LG"
    ],
    "doi": "10.1007/978-3-031-46221-4_10",
    "journal_ref": "Studies in Computational Intelligence (2023), vol 1119. pp 205-222, Springer, Cham",
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "One-Shot Identification with Different Neural Network Approaches",
    "url": "https://arxiv.org/abs/2601.08278",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "03ffad71-7532-4da0-a200-a1829eef43d4",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}