PyTorch implementation of CDAL "Contextual Diversity for Active Learning" accepted in ECCV20.
Himanshu Arora, Sharat Agarwal, Saket Anand, Chetan Arora
First two authors contributed equally
Requirement of large annotated datasets restrict the use of deep convolutional neural networks (CNNs) for many practical applications. The problem can be mitigated by using active learning (AL) techniques which, under a given annotation budget, allow to select a subset of data that yields maximum accuracy upon fine tuning. State of the art AL approaches typically rely on measures of visual diversity or prediction uncertainty, which are unable to effectively capture the variations in spatial context. On the other hand, modern CNN architectures make heavy use of spatial context for achieving highly accurate predictions. Since the context is difficult to evaluate in the absence of ground-truth labels, we introduce the notion of contextual diversity that captures the confusion associated with spatially co-occurring classes. Contextual Diversity (CD) hinges on a crucial observation that the probability vector predicted by a CNN for a region of interest typically contains information from a larger receptive field. Exploiting this observation, we use the proposed CD mea- sure within two AL frameworks: (1) a core-set based strategy and (2) a reinforcement learning based policy, for active frame selection. Our exten- sive empirical evaluation establish state of the art results for active learn- ing on benchmark datasets of Semantic Segmentation, Object Detection and Image classification. Our ablation studies show clear advantages of using contextual diversity for active learning.
- Python 3.6
- Pytorch >= 0.4.1
- CUDA 9.0 or higher
- CPU compatible but NVIDIA GPU + CUDA CuDNN is recommended.
Clone the repo:
$ git clone https://github.com/harora/CDAL
$ cd CDALBy default, logs are stored in <root_dir>/log with this structure:
<root_dir>/experiments/logsSample features in features folder for PASCAL-VOC.
For weighted features:
python preprocess.pyChange the path to raw features in the preprocess.py
Creates a folder <root_dir>/features2 with weighted features.
For CDAL-RL selection:
python main.py --number_of_picks <number of frames to select> --path_to_features <path to weighted features> --classes <number of classes in dataset> --gpu 1 --save-dir log/summe-split0 --start_idx 0List of selected samples will be stored in <root_dir>/selection/
This codebase is borrwed from VSUMM