From Data to Optimization: Data-Free Deep Incremental Hashing with Data Disambiguation and Adaptive Proxies

Deep incremental hashing methods require a large number of original training samples to preserve old knowledge. However, the old training samples are not always available. This "data-free" setting poses great challenges for learning discriminative codes for new classes (plasticity) and maintaining the code invariance of old ones (stability). On the one hand, the presence of ambiguous data in new-emerging classes, which is highly similar to that in old classes, further aggravates catastrophic forgetting. On the other hand, although well-separated hash codes of new classes can be learned by forcing them towards fixed hash centers, it may significantly change the learned parameters of the old model, leading to severe forgetting on old classes. To alleviate the stability-plasticity dilemma in data-free situations, this paper presents a novel deep incremental hashing method called Data-Free Deep Incremental Hashing (DFIH) from the data to the optimization aspect. We start from the data aspect and propose a data disambiguation module to reveal and discard ambiguous data, especially pixels to alleviate the forgetting issues. Subsequently, we introduce a set of trainable hash proxies during the optimization process. These proxies are optimized adaptively as well as the hash codes, not only guiding the model to learn discriminative hash codes for new classes but also avoiding the dramatic modification of the model's parameters, thus improving plasticity and maintaining stability. Extensive experiments on six widely-used image retrieval benchmarks and sixteen incremental learning situations show the superiority of DFIH. Ablation analysis further confirms the effectiveness of the components in DFIH. The code of this work is released at https://github.com/SuQinghang/DFIH .