One loss for all: Deep hashing with a single cosine similarity based learning objective
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 …
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 …
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective
J Tian Hoe, K Woh Ng, T Zhang, CS Chan… - arXiv e …, 2021 - ui.adsabs.harvard.edu
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 a single learning objective. Specifically, we show that maximizing …
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 a single learning objective. Specifically, we show that maximizing …
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