@inproceedings{bjerge2024fewshot,
    type = {inproceedings},
    key = {bjerge2024fewshot},
    title = {Few-Shot Learning with Novelty Detection},
    author = {Kim Bjerge and Paul Bodesheim and Henrik Karstoft},
    booktitle = {International Conference on Deep Learning Theory and Applications (DeLTA)},
    year = {2024},
    note = {Best Paper Award},
    abstract = {Machine learning has achieved considerable success in data-intensive applications, yet encounters challenges when confronted with small datasets. Recently, few-shot learning (FSL) has emerged as a promising solution to address this limitation. By leveraging prior knowledge, FSL exhibits the ability to swiftly generalize to new tasks, even when presented with only a handful of samples in an accompanied support set. This paper extends the scope of few-shot learning by incorporating novelty detection for samples of categories not present in the support set of FSL. This extension holds substantial promise for real-life applications where the availability of samples for each class is either sparse or absent. Our approach involves adapting existing FSL methods with a cosine similarity function, complemented by the learning of a probabilistic threshold to distinguish between known and outlier classes. During episodic training with domain generalization, we introduce a scatter loss function designed to disentangle the distribution of similarities between known and outlier classes, thereby enhancing the separation of novel and known classes. The efficacy of the proposed method is evaluated on commonly used FSL datasets and the EU Moths dataset characterized by few samples. Our experimental results showcase accuracy, ranging from 95.4% to 96.7%, as demonstrated on the Omniglot dataset through few-shot-novelty learning (FSNL). This high accuracy is observed across scenarios with 5 to 30 classes and the introduction of novel classes in each query set, underscoring the robustness and versatility of our proposed approach.},
    groups = {noveltydetection},
    url = {https://www.insticc.org/node/TechnicalProgram/delta/2024/presentationDetails/127876},
}