Anomaly detection refers to finding observations which do not conform to expected behavior. It is widely applied in many domains such as image processing, fraud detection, intrusion detection, medical health, etc. However, most of the anomaly detection techniques focus on detecting a single anomalous instance. Such techniques fail when there is only a slight difference between the anomalous instance and a non-anomalous instance. Various collective anomaly detection techniques (based on clustering, deep learning, etc) have been developed that determine whether a group of records form an anomaly even though they are only slightly anomalous instances. However, they do not provide any information about the attributes that make the group anomalous. In other words, they are focussed only on detecting records that are collectively anomalous and are not able to detect anomalous patterns in general. FGSS is a scalable anomalous pattern detection technique that searches over both records and attributes. However, FGSS has several limitations preventing it from functioning on continuous, unstructured and high dimensional data such as images, etc. We propose a general framework called DeepFGSS, which uses Autoencoder, enabling it to operate on any kind of data. We evaluate its performance using four experiments on both structured and unstructured data to determine its accuracy of detecting anomalies and efficiency of distinguishing between datasets containing anomalies and ones that do not.