sae-pytorch

Original : [MATLAB version]

PyTorch implementation of Semantic AutoEncoder (SAE).

How to Run

1. git clone https://github.com/hoseong-kim/sae-pytorch.git
2. Download 'awa_demo_data.mat'
   • https://drive.google.com/file/d/1l0UVhhIU-SmtJ9hqk7OVOG9zNga9qt_I/view
3. python sae.py

An Implementation of SAE in PyTorch

1. Set CUB, AwA, aP&Y, SUN, and ImageNet datasets.
   • Partially done (only for AwA dataset).
   • Other datasets will also be available soon.
2. Extract deep features from various deep models, e.g., AlexNet, VGG16, VGG19, GoogleNet, Inception_v3, ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152.
   • Done, but tuning my source code to achieve results in this paper.
   • The source code will be available after reproducing.
3. Train a Semantic AutoEncoder (SAE).
   • Done.
4. Test unseen class data.
   • Done.

Release Note

v1.0

• Bug fix

Download Paper

Semantic Autoencoder for Zero-shot Learning: [Paper Link (arXiv)]