It is also possible to watch the graphs update in a new window during training. The loss tracker object also enables pre-programmed heuristics to address issues such as overfitting, underfitting, unknown fitting, and drift. The loss tracker object produces graphs of the changes in losses over epochs and saves them, again to allow a human to analyze and verify the training session. It can be seen that centered, frontal images with natural postures reconstruct more accurately than others.Īlso, rare objects such as glasses, jewelry and watermarks are subdued.Īt the end of each epoch, all of the calculated losses are passed to a class I designed, called Loss Tracker. Original images are on the right and generated images are on the left. The losses on the validation batch are expected to decrease at each epoch similarly to their change in the rest of the dataset.Īfter every epoch, an image comparing the original validation images with the reconstructed images is saved to the epoch's folder, allowing a human eye to monitor the training session. A special utility was written, the UTKFace Labeler, which sorts the dataset images to separated folders based on the label, to match with PyTorch demands that classes are determined by folders structure.īefore training, one random batch of images is separated from the dataset and used for validation, meaning that the network does not back propagate losses on it. UTKFace contains over 20,000 aligned and cropped face images withtheir appropriate labels. Python data science and graphic packages: NumPy, scikit-learn, OpenCV, imageio and Matplotlibįor training, the UTKFace dataset is used, which was collected by the original authors of the article and tested in their implementation.Viewing from left to right,labeled Z vectors of size 70 in a latent space are transformed into face images of dimensions 128x128x3.ĭiscriminator on Z with 4 fully connected layers.ĭiscriminator on images with 4 convolutional layers and 2 fully connected layers.
AGE PROGRESSION WEBSITE GENERATOR
Generator with 7 deconvolutional layers and a fully connected layer. Viewing from left to right, faceimages of dimensions 128x128x3 are transformed into unlabeled Z vectors of size 50 in a latent space. EncoderĮncoder with 5 convolutional layers and a fully connected layer. The network is comprised of an encoder which transforms RGB images to Z vectors (vectors in a latent space), a generator which transforms vectors to RGB images, a discriminator that measures (and forces) uniform distribution on the encoder's output and a discriminator that measures (and forces) realistic properties on the generator's output. Other external packages that wereused are NumPy, scikit-learn, OpenCV, imageio and Matplotlib.
AGE PROGRESSION WEBSITE CODE
The system architecture was written in Python 3.7 and PyTorch 0.4.1, with attempts to keep the code ascompatible as possible with older versions of Python 3 and PyTorch. The article presented a method to performage modification on a given face image, with exciting utilization from recreational applications to assist the searches of missing children.Ī complete review of the project can be seen here. Skimming through articles, I discovered an interesting paper from CVPR 2017, titled Age Progression/Regression by Conditional Adversarial Autoencoder (Zhang, Song, et al.). In the latter part of the course, I was asked to choose a paper to study and implement. PyTorch Implementation of Age Progression/Regression by Conditional Adversarial AutoencoderĪs a participant of the 2018 Workshop in Machine Learning Applications for Computer Graphics (Cohen-Or, Fogel), I was exposed to many interesting ideas in the fields of artificial intelligence and computer vision, such as variational autoencoders (VAE) and deep convolutional generative adversarial networks (DCGAN).
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