"Metric Learning with Adaptive Density Discrimination" introduced a new optimization objective for distance metric learning called Magnet Loss that, unlike related losses, operates on entire neighborhoods in the representation space and adaptively defines the similarity that is being optimized to account for the changing representations of the training data.
This implementation requires Tensorflow>=1.0.
The Magnet Loss function is implemented in magnet_ops.py. The function magnet_loss implements the exact
loss function and requires class and cluster labels for each example. This formulation allows varying numbers
of clusters per class. The minibatch_magnet_loss is a thin wrapper around magnet_loss that assumes a
particular batch structure as described in section 3.2 of the paper. This minibatch loss, when combined with
the sampling procedure for batches described in the paper, is a stochastic approximation of the exact loss.
The module magnet_tools.py contains a ClusterBatchBuilder class that encapsulates the state and logic for
computing, maintaining, and sampling from a cluster index of the training data. This implementation has not
been performance optimized to a great degree and perhaps some of it could be implemented most efficiently
in Tensorflow.
A simple experiment of MNIST shows that the implementation works, but isn't an interesting case for comparison to other losses since MNIST is too simple.
More experiments are in progress.
Feel free to submit PRs to correct anything or add functionality or experiments!