1,153 research outputs found
Deep Metric Learning with Chance Constraints
Deep metric learning (DML) aims to minimize empirical expected loss of the
pairwise intra-/inter- class proximity violations in the embedding image. We
relate DML to feasibility problem of finite chance constraints. We show that
minimizer of proxy-based DML satisfies certain chance constraints, and that the
worst case generalization performance of the proxy-based methods can be
characterized by the radius of the smallest ball around a class proxy to cover
the entire domain of the corresponding class samples, suggesting multiple
proxies per class helps performance. To provide a scalable algorithm as well as
exploiting more proxies, we consider the chance constraints implied by the
minimizers of proxy-based DML instances and reformulate DML as finding a
feasible point in intersection of such constraints, resulting in a problem to
be approximately solved by iterative projections. Simply put, we repeatedly
train a regularized proxy-based loss and re-initialize the proxies with the
embeddings of the deliberately selected new samples. We apply our method with
the well-accepted losses and evaluate on four popular benchmark datasets for
image retrieval. Outperforming state-of-the-art, our method consistently
improves the performance of the applied losses. Code is available at:
https://github.com/yetigurbuz/ccp-dmlComment: Under review at IEEE Transactions on Neural Networks and Learning
System
Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach for Feature Embedding
Unsupervised Deep Distance Metric Learning (UDML) aims to learn sample
similarities in the embedding space from an unlabeled dataset. Traditional UDML
methods usually use the triplet loss or pairwise loss which requires the mining
of positive and negative samples w.r.t. anchor data points. This is, however,
challenging in an unsupervised setting as the label information is not
available. In this paper, we propose a new UDML method that overcomes that
challenge. In particular, we propose to use a deep clustering loss to learn
centroids, i.e., pseudo labels, that represent semantic classes. During
learning, these centroids are also used to reconstruct the input samples. It
hence ensures the representativeness of centroids - each centroid represents
visually similar samples. Therefore, the centroids give information about
positive (visually similar) and negative (visually dissimilar) samples. Based
on pseudo labels, we propose a novel unsupervised metric loss which enforces
the positive concentration and negative separation of samples in the embedding
space. Experimental results on benchmarking datasets show that the proposed
approach outperforms other UDML methods.Comment: Accepted in BMVC 202
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