Abstract: An electronic component according to an embodiment of the present invention includes: a chip; a die pad to which the chip is secured; a suspension terminal extending from the die pad; a lead terminal electrically connected to the chip; and a dummy terminal, in which the suspension terminal is disposed closer to the dummy terminal than the lead terminal.

Abstract: A method of manufacturing an inductor component includes preparing an insulating paste that is photosensitive and that includes a filler material composed of quartz, a glass material and a resin material, and a conductive paste, forming a first insulating layer by applying the insulating paste, and exposing the first insulating layer in a state where a first portion of the first insulating layer is shielded by a mask. The method further includes removing the first portion of the first insulating layer to form a groove at a position corresponding to the first portion, applying the conductive paste in the groove to form a coil conductor layer in the groove, and applying the insulating paste on the first insulating layer and the coil conductor layer to form a second insulating layer.

Abstract: An image processing device includes: a memory; and a processor coupled to the memory and configured to: determine whether or not overflow occurs in a virtual buffer when image data of each frame of moving image data is encoded; refer to recognition object information in a case where it is determined that the overflow occurs; and change a quantization value of a block at a position that corresponds to an area of an object to be recognized other than an object to be recognized specified by the recognition object information among objects to be recognized included in the image data to a quantization value higher than a quantization value that corresponds to a limit compression ratio.

Abstract: An image processing device includes: a memory; and a processor coupled to the memory and configured to: acquire a first feature map output from a hidden layer by forward propagation of image data; acquire a plurality of second feature maps output from the hidden layer by forward propagation of each of a plurality of pieces of decoded data obtained by sequentially encoding the image data by using different quantization values and thereafter decoding the encoded image data; calculate a degree of influence of each block of the image data on a recognition result by backpropagating each error between the first feature map and the plurality of second feature maps; and determine a quantization value of each block when the image data is encoded.

Abstract: An analysis device includes: a memory; and a processor coupled to the memory and configured to: decide a first compression level based on a degree of influence of each area on a recognition result of a case where recognition processing is performed for each image data after a change in image quality; in a case where image data compressed at a second compression level according to the first compression level is decoded, perform the recognition processing for decoded data and calculate a recognition result; and determine at which compression level of the first compression level or the second compression level image data is compressed according to the calculated recognition result.

Abstract: An encoding system includes: a memory; and a processor coupled to the memory and configured to: calculate, for each area, for a first image, a quantization value that has a compression ratio according to a degree of influence on recognition accuracy during recognition processing; set, when setting the quantization value calculated for each area, for each area of a second image that is acquired after the first image, a quantization value that has a compression ratio lower than the compression ratio, for a specific area other than an area that corresponds to an area of an object to be recognized included in the first image; and encode the second image, using the quantization value.

Abstract: An image processing device includes: a memory; and a processor coupled to the memory and configured to: calculate a degree of influence of each pixel of image data, the influence being exerted on a processing result when the image data is input to a deep learning model; reduce an information amount of intermediate information extracted from the deep learning model based on the degree of influence; and compress the intermediate information, the information amount of which has been reduced.

Abstract: A server includes: a memory; and a processor coupled to the memory and configures to: receive a frame image of a moving image compressed by an information processing device; detect a sign of deterioration in analysis accuracy based on accuracy information that represents certainty of an analysis result by artificial intelligence for the frame image; transmit, to the information processing device, instruction information of controlling a compression rate in compression of the moving image by the information processing device between an upper bound and a lower bound; and change the upper bound and the lower bound according to stability of the analysis accuracy based on the detection of a sign of deterioration in the analysis accuracy.

Abstract: A bit rate control system includes: a memory; and a processor coupled to the memory and configured to: perform an image recognition process on a frame to be processed in video while changing image quality to specify the image quality at which recognition accuracy of an object included in the frame to be processed reaches an allowable limit; calculate a first quantization step that corresponds to the specified image quality; determine whether or not overflow occurs in a virtual buffer when encoding processing is performed on the frame to be processed by using the calculated first quantization step; and exercise control to perform the encoding processing on the frame to be processed by using the calculated first quantization step when the overflow is determined not to occur.

Abstract: An image processing device includes: a memory; and a processor coupled to the memory and configured to: calculate, in a case where an image quality of image data is changed, recognition accuracy of an object included in each piece of the image data that has been changed; change, in the image data, a region that includes the object to have an image quality with which the recognition accuracy becomes a predetermined allowable limit and to change a region other than the region that includes the object to have an image quality with which the recognition accuracy becomes less than the predetermined allowable limit; and input, into an encoder, the image data that has been changed.

Abstract: A method includes: generating a refine image having a maximized correct label score of inference from an incorrect image from which an incorrect label is inferred by a neural network; generating a third map by superimposing a first map and a second map, the first map indicating pixels to each of which a change is made in generating the refine image, of a plurality of pixels of the incorrect image, the second map indicating a degree of attention for each local region in the refine image, the each local region being a region that has drawn attention by the neural network; and specifying a set of pixels that cause incorrect inference in the incorrect image by calculating a pixel value of the third map for each set of pixels, wherein the map generating processing adjusts the second map based on appearance frequency of each degree of attention.

Abstract: An image processing apparatus, includes a memory; and a processor coupled to the memory and configured to: generate a trained machine learning model by learning a machine learning model using a first set of image data, output an inference result by inputting a second set of image data to the trained machine learning model, and process a region of interest at a time of inference with respect to image data for which an inference result is correct in the second set of image data.

Abstract: An image processing system includes: a memory; and a processor coupled to the memory and configured to: generate information that indicates a feature portion that affects image recognition processing, by executing image recognition processing on first image data acquired at a first time; predict information that indicates the feature portion at a second time after the first time, based on the information that indicates the feature portion at the first time; and encode second image data acquired at the second time, by using a compression rate based on the predicted information that indicates the feature portion.

Abstract: An analysis device includes a processor configured to: execute a first learning process on a generative model for images such that the images that bring a recognition result of an image recognition process into a preassigned state are generated; execute a second learning process on the generative model on which the first learning process has been executed, while gradually changing recognition accuracy of the images generated by the generative model on which the first learning process has been executed, to desired recognition accuracy; acquire each piece of information on back-error propagation calculated by executing the image recognition process, for the images with each level of the recognition accuracy generated through a course of the second learning process; and generate evaluation information indicating each of image parts that cause erroneous recognition at each level of the recognition accuracy, based on the acquired each piece of the information on the back-error propagation.

Abstract: An analysis device includes: a memory; and a processor coupled to the memory and configured to: execute a first learning process on a generative model for images such that the images that bring a recognition result of an image recognition process into a preassigned state are generated; execute a second learning process on the generative model on which the first learning process which has been executed such that recognition accuracy of the images generated by the generative model on which the first learning process has been executed matches desired recognition accuracy; acquire information on back-error propagation calculated by executing the image recognition process, for the images with the desired recognition accuracy generated by executing the second learning process; and generate evaluation information that indicates image parts that cause over-detection at the desired recognition accuracy, based on the acquired information on the back-error propagation.

Abstract: An analysis method implemented by a computer includes: generating a refine image by changing an incorrect inference image such that a correct label score of inference is maximized, the incorrect inference image being an input image when an incorrect label is inferred in an image recognition process; and narrowing, based on a score of a label, a predetermined region to specify an image section that causes incorrect inference, the score of the label being inferred by inputting to an inferring process an image obtained by replacing the predetermined region in the incorrect inference image with the refine image.

Abstract: A data processing device includes: a memory; and a processor coupled to the memory and configured to: in a case where a compression level is designated based on a degree of influence of each block on a recognition result when a recognition process is performed on image data, generate compressed data by performing a compression process on the image data by using the compression level; and in a case where the recognition result when the recognition process is performed on decoded data obtained by decoding the compressed data satisfies a predetermined condition, correct a block that corresponds to a recognition target, in a direction of raising the compression level.

Abstract: An analysis device includes: a memory; and a computer coupled to the memory and configured to: store information that indicates a degree of influence of each area of each piece of decoded data on recognition results and is calculated by performing a recognition process on the decoded data obtained by decoding each piece of compressed data when a compression process is performed on image data at different compression levels; and designate the compression levels for each area of the image data, based on the information that corresponds to the different compression levels and indicates the degree of influence of each area of each piece of the decoded data on the recognition results.

Abstract: A method includes: generating a refine image having a maximized correct label score of inference from an incorrect image by which an incorrect label is inferred by a neural network; generating a third map by superimposing a first map and a second map, the first map indicating pixels to each of which a change is made in generating the refine image, of plural pixels of the incorrect image, the second map indicating a degree of attention for each local region in the refine image, the each local region being a region that has drawn attention at the time of inference by the neural network, and the third map indicating a degree of importance for each pixel for inferring a correct label; and specifying an image section based on a pixel value of the third map, the image section corresponding to a region causing incorrect inference in the incorrect image.

Abstract: An image processing system includes one or more memories; and one or more processors coupled to the one or more memories and the one or more processors configured to acquire a degree of influence of each region of image data on a recognition result when recognition processing is performed for the image data, and reduce data size of the image data based on the degree of influence.