Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes
Authors: Sara Sangalli, Ertunc Erdil, Andeas Hötker, Olivio Donati, Ender Konukoglu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental results for image-based binary and multi-class classification applications using an in-house medical imaging dataset, CIFAR10, and CIFAR100. |
| Researcher Affiliation | Academia | 1 Computer Vision Lab, ETH Zürich 2 Institute for Diagnostic and Interventional Radiology, Universitätsspital Zürich |
| Pseudocode | Yes | Algorithm 1 ALM for Training DNNs |
| Open Source Code | Yes | 1Code is available at: https://github.com/salusanga/alm-dnn. |
| Open Datasets | Yes | CIFAR10 and CIFAR100 [11] |
| Dataset Splits | Yes | In our experiments, we randomly split 20% of the training cohort as validation set by keeping the class imbalance consistent across the datasets. |
| Hardware Specification | Yes | The proposed method is implemented in Py Torch and we run all experiments on a Nvidia Ge Force GTX Titan X GPU with 12GB memory. |
| Software Dependencies | No | The paper states 'The proposed method is implemented in Py Torch' but does not specify the version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Hyper-parameters selection: Selection of the best hyper-parameters is crucial both to ensure proper and fair evaluation of the methods and to understand the true performance of any model. To achieve this, we perform grid-search to determine the hyper-parameters that yield the highest AUC for the binary experiments. ... In the proposed method, there are 4 parameters to be set: µ(0), λ(0), ρ, and δ. Thus, hyperparameters search is an important aspect of the proposed method. ... We initialize all the Lagrangian multipliers λ(0) i to 0. We choose µ(0) from the set {10 7, 10 6, 10 5, 10 4, 10 3}, as it is suggested to choose a small value in the beginning and increase it iteratively using the equation µ(k+1) = ρ µ(k). We choose ρ from the set {2, 3} as ρ > 1 is suggested. |