One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning ObjectiveDownload PDF

Published: 09 Nov 2021, Last Modified: 01 Oct 2023NeurIPS 2021 PosterReaders: Everyone
Keywords: hashing, deep hashing, learning to hash, image retrieval, instance level retrieval
TL;DR: We unify deep hashing under a single classification objective, in the lens of cosine similarity.
Abstract: A deep hashing model typically has two main learning objectives: to make the learned binary hash codes discriminative and to minimize a quantization error. With further constraints such as bit balance and code orthogonality, it is not uncommon for existing models to employ a large number (>4) of losses. This leads to difficulties in model training and subsequently impedes their effectiveness. In this work, we propose a novel deep hashing model with only $\textit{a single learning objective}$. Specifically, we show that maximizing the cosine similarity between the continuous codes and their corresponding $\textit{binary orthogonal codes}$ can ensure both hash code discriminativeness and quantization error minimization. Further, with this learning objective, code balancing can be achieved by simply using a Batch Normalization (BN) layer and multi-label classification is also straightforward with label smoothing. The result is a one-loss deep hashing model that removes all the hassles of tuning the weights of various losses. Importantly, extensive experiments show that our model is highly effective, outperforming the state-of-the-art multi-loss hashing models on three large-scale instance retrieval benchmarks, often by significant margins.
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