Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing
Authors: Renyu Zhang, Aly A Khan, Robert L. Grossman, Yuxin Chen
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we propose Batch-BALANCE, a scalable batch-mode active learning algorithm... We show that our algorithm can effectively handle realistic multi-class classification tasks, and achieves compelling performance on several benchmark datasets for active learning under both lowand large-batch regimes. |
| Researcher Affiliation | Academia | Renyu Zhang1, Aly A. Khan2,3, Robert L. Grossman1,4, Yuxin Chen1 1Department of Computer Science, University of Chicago 2Department of Pathology, University of Chicago 3Department of Family Medicine, University of Chicago 4Department of Medicine, University of Chicago |
| Pseudocode | Yes | Algorithm 1 Active selection w/ Batch-BALANCE; Algorithm 2 Greedy Selection; Algorithm 3 BALANCE-Clustering |
| Open Source Code | Yes | Reference code is released at https://github.com/zhangrenyuuchicago/BALan Ce. |
| Open Datasets | Yes | In the main paper, we consider four datasets (i.e. MNIST (Le Cun et al., 1998), Repeated MNIST (Kirsch et al., 2019), Fashion-MNIST (Xiao et al., 2017) and EMNIST (Cohen et al., 2017)) as benchmarks for the small-batch setting, and two datasets (i.e. SVHN (Netzer et al., 2011), CIFAR (Krizhevsky et al., 2009)) as benchmarks for the large-batch setting. |
| Dataset Splits | Yes | We split each dataset into unlabeled AL pool Dpool, initial training dataset Dtrain, validation dataset Dval, test dataset Dtest and unlabeled dataset Dpool. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' but does not specify any software libraries or frameworks (e.g., PyTorch, TensorFlow) with their version numbers. |
| Experiment Setup | Yes | To avoid overfitting, we train the BNNs with MC dropout at each iteration with early stopping. for MNIST, Repeated-MNIST, EMNIST, and Fashion MNIST, we terminate the training of BNNs with patience of 3 epochs. For SVHN and CIFAR-10, we terminate the training of BNNs with patience of 20 epochs. [...] We use Adam optimizer (Kingma & Ba, 2017) for all the models in the experiments. For c SG-MCMC, we use Res Net-18 (He et al., 2016) and run 400 epochs in each AL iteration. We set the number of cycles to 8 and initial step size to 0.5. [...] VGG-11 is trained using SGD with fixed learning rate 0.001 and momentum 0.9. |