GALAXY: Graph-based Active Learning at the Extreme
Authors: Jifan Zhang, Julian Katz-Samuels, Robert Nowak
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we demonstrate GALAXY s superiority over existing state-of-art deep active learning algorithms in unbalanced vision classification settings generated from popular datasets. We conduct experiments under 8 different class imbalance settings. |
| Researcher Affiliation | Academia | 1University of Wisconsin, Madison, USA. Correspondence to: Jifan Zhang <jifan@cs.wisc.edu>. |
| Pseudocode | Yes | Algorithm 1 S2: Shortest Shortest Path. Algorithm 2 Build Graph. Algorithm 3 Connect: build higher order edges. Algorithm 4 GALAXY. |
| Open Source Code | Yes | Code can be found in https://github.com/jifanz/GALAXY. |
| Open Datasets | Yes | We generate the extremely unbalanced settings for both binary and multi-class classification from popular vision datasets CIFAR-10(Krizhevsky et al., 2009), CIFAR-100(Krizhevsky et al., 2009), Path MNIST(Yang et al., 2021) and SVHN(Netzer et al., 2011). |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It mentions using 'the pool' and evaluating 'over the pool', but no specific split percentages or counts for training, validation, or test sets are detailed. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. It only mentions 'training deep learning systems'. |
| Software Dependencies | No | The paper mentions 'Res Net-18 model in Py Torch' and 'Adam optimization algorithm' but does not specify version numbers for PyTorch or any other software dependencies, making it difficult to precisely reproduce the software environment. |
| Experiment Setup | Yes | We set B = 100 and T = 50. We use the Res Net-18 model in Py Torch pretrained on Image Net for initialization and cold-start the training for every labeled set L. We use the Adam optimization algorithm with learning rate of 10-2 and a fixed 500 epochs for each L. We use a cross entropy loss weighted by 1/Nk(L) for each class k. |