Cross-Technology Interference (CTI) is one of the major issues that hinder WiFi networks from achieving full spectrum utilization. Interference from nearby ZigBee devices, LTE-U UEs or even microwave ovens could emit RF signals over the frequency partially overlapping with the WiFi band. To combat such CTI, existing solutions have proposed several signal processing algorithms for error recovery or interference cancellation. However, most of those approaches need knowledge about the physical layer structure of CTI, which cannot be applied to denoise the unstructured interference from unknown electronics, e.g., microwave ovens. To overcome this deficiency, we present a CTI suppression framework based on Denoising AutoEncoder (DAE). The DAE is developed to learn the patterns of interference with unknown structures and passively suppress CTI with the zero cost. To avoid the expansive human cost of data collection, we propose a systematic way to synthesize corrupted WiFi signals for model training. Our experiments verify that the model trained with synthesized data can effectively reconstruct real corrupted WiFi signals and improve the decoding success probability.