chainer.functions.discriminative_margin_based_clustering_loss¶
- chainer.functions.discriminative_margin_based_clustering_loss(embeddings, labels, delta_v, delta_d, max_embedding_dim, norm=1, alpha=1.0, beta=1.0, gamma=0.001)[source]¶
Discriminative margin-based clustering loss function
This is the implementation of the following paper: https://arxiv.org/abs/1708.02551 This method is a semi-supervised solution to instance segmentation. It calculates pixel embeddings, and calculates three different terms based on those embeddings and applies them as loss. The main idea is that the pixel embeddings for same instances have to be closer to each other (pull force), for different instances, they have to be further away (push force). The loss also brings a weak regularization term to prevent overfitting. This loss function calculates the following three parameters:
- Variance Loss
Loss to penalize distances between pixels which are belonging to the same instance. (Pull force)
- Distance loss
Loss to penalize distances between the centers of instances. (Push force)
- Regularization loss
Small regularization loss to penalize weights against overfitting.
- Parameters
embeddings (
Variableor N-dimensional array) – predicted embedding vectors (batch size, max embedding dimensions, height, width)labels (N-dimensional array) – instance segmentation ground truth each unique value has to be denoting one instance (batch size, height, width)
delta_v (float) – Minimum distance to start penalizing variance
delta_d (float) – Maximum distance to stop penalizing distance
max_embedding_dim (int) – Maximum number of embedding dimensions
norm (int) – Norm to calculate pixels and cluster center distances
alpha (float) – Weight for variance loss
beta (float) – Weight for distance loss
gamma (float) – Weight for regularization loss
- Returns
Variance loss: Variance loss multiplied by
alphaDistance loss: Distance loss multiplied by
betaRegularization loss: Regularization loss multiplied by
gamma
- Return type