Code for paper “Variational Multi-Task Learning with Gumbel-Softmax Priors” accepted by NeurIPS2021.
- Python 3.6.9
- Pytorch 1.1.0
- GPU: an NVIDIA Tesla V100
Inside this repository, we mainly conduct comprehensive experiments on Office-Home. Download the dataset from the following link, and place it in Dataset/ directory. To split documents are obtained by randomly selecting 5%, 10%, and 20% of samples from each task as the training set and use the remaining samples as the test set. The split documents used for the office-home dataset comes from MRN. There are three training list files called train_5.txt, train_10.txt and train_20.txt and three test list files called test_5.txt, test_10.txt, test_20.txt which are corresponding to the training and test files of 5% data, 10% data and 20% data. More splits like(30%, 40%, ...) used for the ablation study in our paper will be open in the public repository.
- Office-home; [link]
To extract the input features based on VGG16 by using the following command:
python feature_vgg16.py #gpu_id #split
To evaluate with the trained model by:
python test.py #gpu_id #split #load_log_file/split vmtl
The pre-trained parameters of VMTL can be found in Trained_model/directory.
Due to the space limitation, the pre-trained parameters of VMTL-AC would be available in the final repository.
To train the proposed VMTL by running the command:
python train.py #gpu_id #split #log vmtl
To train VMTL with amortized classifiers by:
python train.py #gpu_id #split #log vmtl_ac
We will open the detailed split documents and pre-trained parameters of our models for the other datasets in the final public repository.
This code is build based on the following repositories:
Learning Multiple Tasks with Multilinear Relationship Networks and Variational Autoencoder with Gumbel-Softmax Distribution