DROCC: Deep Robust One-Class Classification
Authors: Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation demonstrates that DROCC is highly effective in two different one-class problem settings and on a range of real-world datasets across different domains: tabular data, images (CIFAR and Image Net), audio, and time-series, offering up to 20% increase in accuracy over the state-of-the-art in anomaly detection. |
| Researcher Affiliation | Collaboration | 1Microsoft Research India 2Stanford University 3NITK Surathkal. |
| Pseudocode | Yes | Algorithm 1 Training neural networks via DROCC |
| Open Source Code | Yes | Code is available at https: //github.com/microsoft/Edge ML. |
| Open Datasets | Yes | CIFAR-10 (Krizhevsky, 2009): Widely used benchmark for anomaly detection, 10 classes with 32 32 images. Image Net-10: a subset of 10 randomly chosen classes from the Image Net dataset (Deng et al., 2009) which contains 224 224 color images. Abalone (Dua & Graff, 2017): Physical measurements of abalone are provided and the task is to predict the age. Arrthythmia (Rayana, 2016): Features derived from ECG and the task is to identify arrhythmic samples. Thyroid (Rayana, 2016): Determine whether a patient referred to the clinic is hypothyroid based on patient s medical data. Epileptic Seizure Recognition (Andrzejak et al., 2001): EEG based time-series dataset from multiple patients. Audio Commands (Warden, 2018): A multiclass data with 35 classes of audio keywords. |
| Dataset Splits | Yes | We use the train-test splits when already available with a 80-20 split for train and validation set. In all other cases, we use random 60-20-20 split for train, validation, and test. |
| Hardware Specification | Yes | The experiments were run on an Intel Xeon CPU with 12 cores clocked at 2.60 GHz and with NVIDIA Tesla P40 GPU, CUDA 10.2, and cu DNN 7.6. |
| Software Dependencies | Yes | The experiments were run on an Intel Xeon CPU with 12 cores clocked at 2.60 GHz and with NVIDIA Tesla P40 GPU, CUDA 10.2, and cu DNN 7.6. |
| Experiment Setup | Yes | The main hyper-parameter of our algorithm is the radius r which defines the set Ni(r). We observe that tweaking radius value around d/2 (where d is the dimension of the input data ) works the best... We fix γ as 2 in our experiments unless specified otherwise. Parameter µ (1) is chosen from {0.5 , 1.0}. We use a standard step size from {0.1, 0.01} for gradient ascent and from {10 2, 10 4} for gradient descent; we also tune the optimizer {Adam, SGD}. |