{
  "id": "2304.06619",
  "version": "v1",
  "published": "2023-04-13T15:40:41.000Z",
  "updated": "2023-04-13T15:40:41.000Z",
  "title": "Class-Incremental Learning of Plant and Disease Detection: Growing Branches with Knowledge Distillation",
  "authors": [
    "Mathieu Pagé Fortin"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "This paper investigates the problem of class-incremental object detection for agricultural applications where a model needs to learn new plant species and diseases incrementally without forgetting the previously learned ones. We adapt two public datasets to include new categories over time, simulating a more realistic and dynamic scenario. We then compare three class-incremental learning methods that leverage different forms of knowledge distillation to mitigate catastrophic forgetting. Our experiments show that all three methods suffer from catastrophic forgetting, but the recent Dynamic Y-KD approach, which additionally uses a dynamic architecture that grows new branches to learn new tasks, outperforms ILOD and Faster-ILOD in most scenarios both on new and old classes. These results highlight the challenges and opportunities of continual object detection for agricultural applications. In particular, the large intra-class and small inter-class variability that is typical of plant images exacerbate the difficulty of learning new categories without interfering with previous knowledge. We publicly release our code to encourage future work.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-04-13T15:40:41.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "knowledge distillation",
      "class-incremental learning",
      "disease detection",
      "growing branches",
      "agricultural applications"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}