Unsupervised Video Anomaly Detection (VAD) has garnered significant attention for its ability to exploit unlabeled videos. However, VAD faces two primary challenges arising from the absence of labels: (i) Striking a balance between overfitting and underfitting, and (ii) Optimal parameter tuning. To tackle these challenges, we propose a novel detector named Flexible AutoEncoder (FlexAE). A fitting-parameter is introduced to regulate the model’s fitting capacity, and a novel Negative Learning (NL) mechanism is integrated to mitigate the influence of anomalies during training. For self-conditioning, a novel algorithm is devised to autonomously update the fitting-parameter and the threshold used in NL based on the reconstruction error. Comprehensive experiments on two benchmark datasets, UCF-Crime and ShanghaiTech, demonstrate that our proposed FlexAE outperforms state-of-the-art methods without the need for manual hyperparameter tuning.