Few-Shot One-Class Classification via Meta-Learning
Authors: Ahmed Frikha, Denis Krompaß, Hans-Georg Köpken, Volker Tresp7448-7456
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on eight datasets from the image and time-series domains show that our method leads to better results than classical OCC and few-shot classification approaches, and demonstrate the ability to learn unseen tasks from only few normal class samples. Moreover, we successfully train anomaly detectors for a real-world application on sensor readings recorded during industrial manufacturing of workpieces with a CNC milling machine, by using few normal examples. |
| Researcher Affiliation | Collaboration | Ahmed Frikha 1, 2, 4, Denis Krompaß 1, 2, Hans-Georg K opken 3, Volker Tresp 2, 4 1Siemens AI Lab 2Siemens Technology 3Siemens Digital Industries 4Ludwig Maximilian University of Munich ahmed.frikha@siemens.com |
| Pseudocode | Yes | Algorithm 1 Meta-training of OC-MAML |
| Open Source Code | Yes | Code available under https://github.com/Ahmed Frikha/Few Shot-One-Class-Classification-via-Meta-Learning |
| Open Datasets | Yes | We evaluate our approach on 8 datasets from the image and time-series domains, including two synthetic time-series (STS) datasets that we propose as a benchmark for FS-OCC on time-series, and a real-world sensor readings dataset of CNC Milling Machine Data (CNCMMD). ... Table 1 shows the results averaged over 5 seeds of the classical OCC approaches (Top) and the meta-learning approaches, namely MAML, FOMAML, Reptile and their one-class versions (Bottom), on 3 image datasets and on the STS-Sawtooth dataset. ... Mini Image Net (MIN), Omniglot (Omn), MT-MNIST with Ttest = T0 and STS-Sawtooth (Saw). |
| Dataset Splits | Yes | To assess the model s adaptation ability to unseen tasks, the available tasks are divided into mutually disjoint task sets: one for meta-training Str, one for metavalidation Sval and one for meta-testing Stest. Each task Ti is divided into two disjoint sets of data, each of which is used for a particular MAML operation: Dtr is used for adaptation and Dval is used for validation, i.e., evaluating the adaptation. ... Algorithm 1: Require: Str: Set of meta-training tasks Require: α, β: Learning rates Require: K, Q: Batch size for the inner and outer updates Require: c: CIR for the inner-updates |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions "some modules of the py Meta library (Spigler 2019)" but does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | Algorithm 1: Require: α, β: Learning rates Require: K, Q: Batch size for the inner and outer updates Require: c: CIR for the inner-updates. ... The cross-entropy loss was used for L. ... We conducted experiments using 5 different seeds and present the average in Table 4. |