We propose a new approach for detecting repeated patterns on a grid in a single image. To do so, we detect repetitions in the space of pre-trained deep CNN filter responses at all layer levels. These encode features at several conceptual levels (from low-level patches to high-level semantics) as well as scales (from local to global). As a result, our repeated pattern detector is robust to challenging cases where repeated tiles show strong variation in visual appearance due to occlusions, lighting or background clutter. Our method contrasts with previous approaches that rely on key point extraction, description and clustering or on patch correlation. These generally only detect low-level feature clusters that do not handle variations in visual appearance of the patterns very well. Our method is simpler, yet incorporates high level features implicitly. As such, we can demonstrate detections of repetitions with strong appearance variations, organized on a nearly-regular axis-aligned grid Results show robustness and consistency throughout a varied database of more than 150 images.