One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective
This work addresses training difficulties for researchers and practitioners in image retrieval by simplifying deep hashing models, though it is incremental as it builds on existing hashing techniques.
The paper tackles the problem of training complexity in deep hashing models, which typically use multiple losses, by proposing a single cosine similarity-based objective that ensures discriminative hash codes and minimizes quantization error. The model outperforms state-of-the-art multi-loss methods on three large-scale instance retrieval benchmarks, achieving significant performance gains.
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 the cosine similarity between the continuous codes and their corresponding 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 an 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. Code is available at https://github.com/kamwoh/orthohash