Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟲𝟴 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗖𝗔𝗖𝗘: 𝗔 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝘁𝗼 𝗥𝗮𝗻𝗸𝗶𝗻𝗴 𝗡𝗲𝘂𝗿𝗼𝗻𝘀 𝗳𝗼𝗿 𝗖𝗡𝗡 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗯𝘆 𝗢𝘅𝗳𝗼𝗿𝗱 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆, 𝗜𝗺𝗽𝗲𝗿𝗶𝗮𝗹 𝗖𝗼𝗹𝗹𝗲𝗴𝗲 𝗼𝗳 𝗟𝗼𝗻𝗱𝗼𝗻 𝗮𝗻𝗱 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 Follow me for a similar post: Ashish Patel  ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗣𝗖𝗔𝗖𝗘: 𝗔 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝘁𝗼 𝗥𝗮𝗻𝗸𝗶𝗻𝗴 𝗡𝗲𝘂𝗿𝗼𝗻𝘀 𝗳𝗼𝗿 𝗖𝗡𝗡 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 🔸 This paper is published arxiv2021. 🔸 PCACE algorithm: a new statistical method that combines the Principal Component Analysis for dimensionality reduction with the Alternating Conditional Expectation algorithm to find the maximal correlation coefficient between a hidden neuron and the final class score. PCACE returns a number between 0 and 1 that indicates the strength of the non-linear relationship between the multiple predictor variables (each element in the activation matrix of a particular channel when feeding multiple input images into the network) and the response variable (each correct final class score before the softmax function when feeding multiple input images into the network). ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 In this paper we introduce a new problem within the growing literature of interpretability for convolution neural networks (CNNs).  🔸 While previous work has focused on the question of how to visually interpret CNNs, we ask what it is that we care to interpret, that is, which layers and neurons are worth our attention? Due to the vast size of modern deep learning network architectures, automated, quantitative methods are needed to rank the relative importance of neurons so as to provide an answer to this question.  🔸 We present a new statistical method for ranking the hidden neurons in any convolutional layer of a network.  🔸 We define importance as the maximal correlation between the activation maps and the class score.  🔸 We provide different ways in which this method can be used for visualization purposes with MNIST and ImageNet and show a real-world application of our method to air pollution prediction with street-level images #computervision #artificialintelligence #innovation