Learning from Candidate Examples

This repository provides code for reproducing the figures in the paper:

"Leveraging inductive bias of neural networks for learning without explicit human annotations", by Fatih Furkan Yilmaz and Reinhard Heckel

Downloading the dataset

Use the following dropbox link to download numpy files containing test and train sets, specifically, our candidate train set, the CIFAR-10.1 test set, and the original CIFAR-10 test and training datasets. Provide the directory of the files to the root argument in the train_reproducible notebook.

candidate.npz Contains the candidate training set (which we constructed from the TinyImages dataset from Antonio Torralba, Rob Fergus, William T. Freeman as described in our paper), as well as the CIFAR 10.1 test set from Ben Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar.
cifar.npz Contains the original CIFAR-10 dataset from Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.
sims.npz Contains the similar images excluded from the training set.

Training on candidate dataset

The code for training models on candidate or CIFAR-10 training sets is given in the train_reproducible notebook. The following variables can be adjusted for different experiments:

root: Directory (pathlib) where the .npz files of the dataset reside.
main_file: Filename for the candidate dataset.
sub_file (optional): Filename for the CIFAR-10 dataset. Primary use is to track the accuracy on a clean subset of candidate dataset.
model: PyTorch model, from {'resnet', 'densenet', 'vgg', 'shake', 'pyramidnet', 'resnext'}.
outpath: Output file for storing training logs.
epochs: # epochs.
print_freq: Frequency for console log.
gpu: GPU for Cuda. If not given, all available Cuda devices will be used.
batch_size: Training batch size. Default values are chosen with respect to maximum 12GiB memory.

Visualizing results

The code for plotting the training results from the log files is given in the visualize_train_logs notebook.

Inspecting similar images

The code for visually inspecting similar images is given in the inspecting_similar_images notebook. The test image with the index provided to test_ind variable is shown along with the top-100 closest images from the candidate set including the eliminated images.

Images that are eliminated from the candidate set upon manual inspection are marked as 'Excluded'.

Source codes

Acknowledgements for PyTorch implementations of the models and examples are as follows:

Training code

The code for training has been incorporated and modified from the official PyTorch examples repository https://github.com/pytorch/examples/.... The default hyperparameters has been used for fair experiments and comparison.

Models

ResNet-18: https://github.com/kuangliu/pytorch-cifar
DenseNet-BC-100: https://github.com/hysts/pytorch_image_classification

Citation

@article{yilmaz_heckel_2019,
    author    = {Fatih Furkan Yilmaz and Reinhard Heckel},
    title     = {Leveraging inductive bias of neural networks for learning without explicit human annotations},
    journal   = {arXiv:1910.09055},
    year      = {2019}
}

Licence

All files are provided under the terms of the Apache License, Version 2.0.

MLI-lab / candidate_training