Deep Visual Analogy-Making
Tensorflow implementation of Deep Visual Analogy-Making. The matlab code of the paper can be found here.
This implementation contains a deep network trained end-to-end to perform visual analogy making with
- Fully connected encoder & decoder networks
- Analogy transformations by vector addition and deep networks (vector multiplication is not implemented)
- Regularizer for manifold traversal transformations
This implementation conatins:
- Analogy transformations of
- with objective for vector-addition-based analogies (L_add)
- with objective for multiple fully connected layers (L_deep)
- with manifold traversal transformations
First, you need to download the dataset with:
To train a model with
normal$ python main.py --dataset shape --is_train True
To test a model with
normal$ python main.py --dataset shape
Result of analogy transformations of
shape dataset with fully connected layers (L_deep) after 1 day of training.
From top to bottom for each : Reference, output, query, target, prediction, manifold prediction after 2 steps, and manifold prediction after 3 steps.
Taehoon Kim / @carpedm20