使用FOMO-AD进行视觉异常检测

Visual Anomaly Detection with FOMO-ADJan JongboomCo-founder & CTOEdge ImpulseLeading development platform for machine learning on edge devices103,933 new projects (!) created since last Embedded Vision Summit40% of these are vision projectsEdge Impulse2Edge Impulse project countCan you trust ML models?3https://medium.com/@damoncivin/arm-at-data-science-africa-2018-1071389e92d9Countering with an 'unknown' state4GiraffeZebraOtherDataset asymmetry5Normal operationFaulty operationDataset asymmetry6Fault state 1Fault state 2Fault state 3Fault state 4Normal operationAnomaly detection7Training dataAll potentialinputsxUnseen input(no anomaly)xUnseen input(anomaly)Auto-encoders?8Input imageReconstructed imagediff w/ input image:similar? no anomaly.•Computationally expensive, need both encoder/decoder.•Working in pixel space is not great: poor evaluation metric, blurry images.•Visual anomaly detection requires very high resolution images.•Same accuracy: 106parameters (auto-encoder) vs 103paramet