Similarity metrics are a core component of many information retrieval and machine learning systems. In this work we propose a method capable of learning a similarity metric from data equipped with a binary relation. By considering only the similarity constraints, and initially ignoring the features, we are able to learn target vectors for each instance using one of several appropriately designed loss functions. A regression model can then be constructed that maps novel feature vectors to the same target vector space, resulting in a feature extractor that computes vectors for which a predefined metric is a meaningful measure of similarity. We present results on both multiclass and multi-label classification datasets that demonstrate considerably faster convergence, as well as higher accuracy on the majority of the intrinsic evaluation tasks and all extrinsic evaluation tasks.
A common theme that unifies all the approaches described thus far is the need to train on pairs (or triples) of instances. This increases the effective size of the training set quadratically, greatly slowing down the training time. Our proposal to decouple the process of learning the embeddings from learning the functions that performs the embedding is not entirely new. The work of @cite_4 utilises a similar two step process for learning a hashing function. In their work the embeddings are in fact bit strings, and the function used to generate the hash codes takes the form of boosted decision trees. They also use a greedy discrete optimisation procedure to find the target hash codes, rather than a numerical optimisation method to find real valued vectors.
@cite_4
2799150641
In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency loss. Our proposed approach employs adversarial training scheme to learn a couple of hash functions enabling translation between modalities while assuming the underlying semantic relationship. To induce the hash codes with semantics to the input-output pair, cycle consistency loss is further delved into the adversarial training to strengthen the correlation between the inputs and corresponding outputs. Our approach is generative to learn hash functions, such that the learned hash codes can maximally correlate each input–output correspondence and also regenerate the inputs so as to minimize the information loss. The learning to hash embedding is thus performed to jointly optimize the parameters of the hash functions across modalities as well as the associated generative models. Extensive experiments on a variety of large-scale cross-modal data sets demonstrate that our proposed method outperforms the state of the arts.