Abstract: A method and an electronic device with image data generating are disclosed. The electronic device includes: one or more processors; and memory storing instructions configured to cause the one or more processors to: input an input image to a target model that performs segmenting on the input image to generate a segmented image whose pixels have respective class labels predicted by the target model, calculate an optimization value for the input image based on the segmented image and based on a class label of a first grid area among a plurality of grid areas of a guide image, and optimize the input image based on the optimization value.

Abstract: Provided are a bispecific antibody and use thereof. The bispecific antibody comprises a B7-H4-targeting antigen-binding domain and a 4-1BB-targeting antigen-binding domain. The bispecific antibody has one or two or three sites for binding to 4-1BB, along with a novel fully human B7-H4 antibody. The bispecific antibody specifically binds to tumor cells by targeting B7-H4, reducing toxicity induced by 4-1BB activation. In addition, the bispecific antibody of the present invention comprises a human Fc fragment, and thus retains the binding of Fc to FcRn and has a longer half-life.

Abstract: A method and apparatus with neural network optimization are provided. A method is performed by a device storing a target network block and processing hardware that performs optimizing for the target network block, the method includes generating, by the processing hardware, an extended network block of the target network block by increasing, a number of channels of a target operation branch in the target network block to a determined number of channels, wherein the target network block includes operation branches that include the target operation branch, and wherein each operation branch includes at least one respective channel, determining importance measures of the respective operation branches, including the target operation branch with the increased number of channels, in the extended network block, and clipping a channel of the target operation branch in the extended network block, the clipping is performed according to the importance measures of the respective operation branches.

Abstract: A method and apparatus for determining a neural network architecture of a processor are provided. The method of determining a target neural network architecture, the method comprising obtaining a first neural network architecture, searching for the first neural network architecture based on a loss function, in response to a first search end condition not being satisfied, and determining a target neural network architecture used in a processor, based on a result of the searching, wherein the loss function is based on a processor computation cost.