CUCL: Codebook for Unsupervised Continual Learning

The focus of this study is on Unsupervised Continual Learning (UCL), as it presents an alternative to Supervised Continual Learning which needs high-quality manual labeled data. The experiments under the UCL paradigm indicate a phenomenon where the results on the first few tasks are suboptimal. This phenomenon can render the model inappropriate for practical applications. To address this issue, after analyzing the phenomenon and identifying the lack of diversity as a vital factor, we propose a method named Codebook for Unsupervised Continual Learning (CUCL) which promotes the model to learn discriminative features to complete the class boundary. Specifically, we first introduce a Product Quantization to inject diversity into the representation and apply a cross quantized contrastive loss between the original representation and the quantized one to capture discriminative information. Then, based on the quantizer, we propose an effective Codebook Rehearsal to address catastrophic forgetting. This study involves conducting extensive experiments on CIFAR100, TinyImageNet, and MiniImageNet benchmark datasets. Our method significantly boosts the performances of supervised and unsupervised methods. For instance, on TinyImageNet, our method led to a relative improvement of 12.76% and 7% when compared with Simsiam and BYOL, respectively.Comment: MM '23: Proceedings of the 31st ACM International Conference on Multimedi

Status of diagnosis and preventative treatment for primary headache disorders: real-world data of unmet needs in China

Abstract Background Headache disorders are widely prevalent and pose a considerable economic burden on individuals and society. Globally, misdiagnosis and inadequate treatment of primary headache disorders remain significant challenges, impeding the effective management of such conditions. Despite advancements in headache management over the last decade, a need for comprehensive evaluations of the status of primary headache disorders in China regarding diagnosis and preventative treatments persists. Methods In the present study, we analyzed the established queries in the Survey of Fibromyalgia Comorbidity with Headache (SEARCH), focusing on previous diagnoses and preventative treatment regimens for primary headache disorders. This cross-sectional study encompassed adults diagnosed with primary headache disorders who sought treatment at 23 hospitals across China between September 2020 to May 2021. Results The study comprised 2,868 participants who were systematically examined. Migraine and tension-type headaches (TTH) constituted a majority of the primary headache disorders, accounting for 74.1% (2,124/2,868) and 23.3% (668/2,868) of the participants, respectively. Medication overuse headache (MOH) affected 8.1% (231/2,868) of individuals with primary headache disorders. Over half of the individuals with primary headache disorders (56.6%, 1,624/2,868) remained undiagnosed. The previously correct diagnosis rates for migraine, TTH, TACs, and MOH were 27.3% (580/2,124), 8.1% (54/668), 23.2% (13/56), and 3.5% (8/231), respectively. The misdiagnosis of “Nervous headache” was found to be the most prevalent among individuals with migraine (9.9%, 211/2,124), TTH (10.0%, 67/668), trigeminal autonomic cephalalgias (TACs) (17.9%, 10/56), and other primary headache disorders (10.0%, 2/20) respectively. Only a minor proportion of individuals with migraine (16.5%, 77/468) and TTH (4.7%, 2/43) had received preventive medication before participating in the study. Conclusions While there has been progress made in the rate of correct diagnosis of primary headache disorders in China compared to a decade ago, the prevalence of misdiagnosis and inadequate treatment of primary headaches remains a veritable issue. As such, focused efforts are essential to augment the diagnosis and preventive treatment measures related to primary headache disorders in the future