TPGAN overview.

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Presentation transcript:

TPGAN overview

Network components: TPGAN overview Global pathway: to generate the overall view of face images Local pathway: to generate the specific facial part: 2 eyes, nose and mouth. The total 4 local pathway networks are independent in parameter. The 4 local pathways are then merged together according to their facial locations Generation convolutions: The concatenated feature maps (local and global) are convoluted several times to generate the face image.

TPGAN overview Loss components: Cross entropy loss Pre-classifier in global pathway to ensure the class information is preserved in encoded vector. Pixel wise loss between generated images and ground truth images to preserve the overall photo-reality of generated images. Symmetry loss between left and right part of generated images to preserve the symmetry. Adversarial loss to discriminate the real and generated image, to make the generated photo more realistic in abstract level. Identity preserving loss for preserving the identity information, by reducing the feature vectors’ distance of ground truth and generated images. Variation regularization loss. (Perceptual losses for real-time style transfer and super-resolution. In ECCV, 2016.) Cross entropy loss

Difficulties in using TPGAN: TPGAN overview Difficulties in using TPGAN: The model contains 6 different loss components. Although the recommended weights are given in paper, there might also exist chances of failure. The model is highly dependent on facial detection. The model probably work on high quality images but may not so satisfactory on vague images. (minor problem) Current progress: Code for model is finished. Data preparation finished. Code for training will be finished soon. Time and equipment requirements: 1 or 2 GPUs for training. After the model is deployed on GPU, it will not take us so much time. The only thing need to be done is to justify the hyper-parameters and result evaluation.

HPEN and TPGAN

Training TPGAN is much more challenging than using HPEN. HPEN and TPGAN TPGAN performs better since GAN is trained with discriminator to preserve the photo-reality of generated images. Training TPGAN is much more challenging than using HPEN. Performance Convenience