BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
Authors: Andreas Kirsch, Joost van Amersfoort, Yarin Gal
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we start by showing how a naive application of the BALD algorithm to an image dataset can lead to poor results in a dataset with many (near) duplicate data points, and show that Batch BALD solves this problem in a grounded way while obtaining favourable results (figure 2). |
| Researcher Affiliation | Academia | Andreas Kirsch Joost van Amersfoort Yarin Gal OATML Department of Computer Science University of Oxford {andreas.kirsch, joost.van.amersfoort, yarin}@cs.ox.ac.uk |
| Pseudocode | Yes | Algorithm 1: Greedy Batch BALD 1 1/e-approximate algorithm |
| Open Source Code | Yes | We provide an open-source implementation2. 2https://github.com/BlackHC/BatchBALD |
| Open Datasets | Yes | We then illustrate Batch BALD s effectiveness on standard AL datasets: MNIST and EMNIST. EMNIST [6] is an extension of MNIST that also includes letters, for a total of 47 classes, and has a twice as large training set. |
| Dataset Splits | Yes | As the labelled dataset is small in the beginning, it is important to avoid overfitting. We do this by using early stopping after 3 epochs of declining accuracy on the validation set. We pick the model with the highest validation accuracy. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU models or CPU specifications. |
| Software Dependencies | No | The paper mentions software like PyTorch and Adam optimizer but does not specify their version numbers, which is required for a reproducible description of software dependencies. |
| Experiment Setup | Yes | Throughout our experiments, we use the Adam [22] optimiser with learning rate 0.001 and betas 0.9/0.999. All our results report the median of 6 trials, with lower and upper quartiles. We use 100 MC dropout samples. We use 10 MC dropout samples. We use 50 MC dropout samples. |