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Adversarial Feature Generation for Zero-Shot Learning

We try to generate synthetic data for unseen classes conditioned on class attributes. Training is done using wasserstein generative adversarial network.

Architecture Diagram

Loss Function

Train

python train.py --logdir run_dir --train-dir /data/CUBNew

Parameters:

  • iterations
  • batch_size
  • dropout
  • train_dir
  • logdir
  • z_dim
  • g_steps
  • d_steps
  • lr
  • wgan
  • log_interval

Results

Data

Link - (https://drive.google.com/file/d/1XIFik0Cv1MTWtQEQQUZygOwL-X-ZajKY/view?usp=sharing)