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Learning to Separate Object Sounds by Watching Unlabeled Video (ECCV 2018)
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data deep MIML network implementation Sep 4, 2018
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options deep MIML network implementation Sep 4, 2018
util deep MIML network implementation Sep 4, 2018
README.md Update README.md Apr 5, 2019
test.py deep MIML network implementation Sep 4, 2018
train.py deep MIML network implementation Sep 4, 2018

README.md

Learning to Separate Object Sounds by Watching Unlabeled Video

Learning to Separate Object Sounds by Watching Unlabeled Video: [Project Page] [arXiv]

This repository contains the deep MIML network implementation for our ECCV 2018 paper.

If you find our code or project useful in your research, please cite:

    @inproceedings{gao2018objectSounds,
      title={Learning to Separate Object Sounds by Watching Unlabeled Video},
      author={Gao, Ruohan and Feris, Rogerio and Grauman, Kristen},
      booktitle={ECCV},
      year={2018}
    }

Use the following command to train the deep MIML network:

python train.py --HDF5FileRoot /your_hdf5_file_root --name deepMIML --checkpoints_dir checkpoints --model MIML --batchSize 256 --learning_rate 0.001 --learning_rate_decrease_itr 5 --decay_factor 0.94 --display_freq 10 --save_epoch_freq 5 --save_latest_freq 500 --gpu_ids 0 --nThreads 2 --num_of_fc 1 --with_batchnorm --continue_train --niter 300 --L 15 --validation_on --validation_freq 50 --validation_batches 10 --selected_classes --using_multi_labels |& tee -a train.log

Acknowlegements

Our code borrows heavily from the the CycleGAN implementation https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/.

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