Deep Active Learning with Adaptive Acquisition
Authors: Manuel Haussmann, Fred Hamprecht, Melih Kandemir
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on three benchmark vision data sets from different domains and complexities: MNIST for images of handwritten digits, Fashion MNIST for greyscale images of clothes, and CIFAR-10 for colored natural images. [...] We summarize the results in Table 1. |
| Researcher Affiliation | Collaboration | 1HCI/IWR, Heidelberg University, Germany 2Bosch Center for Artificial Intelligence, Renningen, Germany |
| Pseudocode | Yes | Algorithm 1: The RAL training procedure |
| Open Source Code | Yes | see github.com/manuelhaussmann/ral for a reference pytorch implementation of the proposed model. |
| Open Datasets | Yes | We evaluate our method on three benchmark vision data sets from different domains and complexities: MNIST for images of handwritten digits, Fashion MNIST for greyscale images of clothes, and CIFAR-10 for colored natural images. |
| Dataset Splits | No | The straight-forward reward would be the performance of the updated predictor on a separate validation set. This, however, clashes with the constraint imposed on us by the active learning scenario. ... Hence, we abandon this option altogether. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions 'pytorch implementation' in a footnote, implying the use of PyTorch, but does not provide specific version numbers for any software dependencies like PyTorch, Python, or CUDA. |
| Experiment Setup | Yes | To evaluate the performance of the proposed pipeline, we take as the predictor is a standard Le Net5 sized model (two convolutional layers of 20, 50 channels and two linear layers of 500, 10 neurons) and as the guide a policy net consisting of two layers with 500 hidden neurons. [...] The predictor is trained for 30 epochs between labeling rounds (labeling five points per round), while the policy net gets one update step after each round. [...] In each experiment the state is constructed by ranking the unlabeled data according to their predictive entropy and then taking every twentieth point until M = 50 points. [...] We stop after having collected 400 points starting from an initial set of 50 data points. |