A method for explainable active learning, to be used for an object detector being pre-trained for detecting objects in an image, by using a deep autoencoder, the method comprising: detecting, by the object detector having as input one or more test images, one or more objects for testing in each of the test images, and outputting, from the object detector, bounding boxes for testing corresponding to the one or more objects for testing; generating first cropped images for testing to n-th cropped images for testing, wherein n is an integer equal to or greater than 1, by cropping regions, respectively corresponding to the bounding boxes for testing, in each of the test images; generating first resized images for testing to (n+1)-th resized images for testing by resizing each of the test images and the first cropped images for testing to the n-th cropped images for testing into a same size; encoding, by a data encoder of the deep autoencoder having as input the first resized images for testing to the (n+1)-th resized images for testing, the first resized images for testing to the (n+1)-th resized images for testing, and outputting, from the data encoder, first data codes for testing to (n+1)-th data codes for testing respectively corresponding to the first resized images for testing to the (n+1)-th resized images for testing, wherein each of the first data codes for testing to the (n+1)-th data codes for testing represents each of subspaces corresponding to each of the first resized images for testing to the (n+1)-th resized images for testing; confirming reference data codes corresponding to the number of the resized images equal to or less than a counter threshold by referring to a data codebook, wherein the data codebook lists the number of the resized images per data code and wherein the data codebook is created or updated by referring to previous training images used for pre-training the object detector; extracting specific data codes for testing, respectively corresponding to the reference data codes, from the first data codes for testing to the (n+1)-th data codes for testing; selecting specific test images, corresponding to the specific data codes for testing, among the test images, as rare samples to be used for re-training the object detector; and updating the data codebook by referring to the specific data codes for testing corresponding to the rare samples. detecting, by the object detector having as input one or more test images, one or more objects for testing in each of the test images, and outputting, from the object detector, bounding boxes for testing corresponding to the one or more objects for testing; generating first cropped images for testing to n-th cropped images for testing, wherein n is an integer equal to or greater than 1, by cropping regions, respectively corresponding to the bounding boxes for testing, in each of the test images; generating first resized images for testing to (n+1)-th resized images for testing by resizing each of the test images and the first cropped images for testing to the n-th cropped images for testing into a same size; encoding, by a data encoder of the deep autoencoder having as input the first resized images for testing to the (n+1)-th resized images for testing, the first resized images for testing to the (n+1)-th resized images for testing, and outputting, from the data encoder, first data codes for testing to (n+1)-th data codes for testing respectively corresponding to the first resized images for testing to the (n+1)-th resized images for testing, wherein each of the first data codes for testing to the (n+1)-th data codes for testing represents each of subspaces corresponding to each of the first resized images for testing to the (n+1)-th resized images for testing; confirming reference data codes corresponding to the number of the resized images equal to or less than a counter threshold by referring to a data codebook, wherein the data codebook lists the number of the resized images per data code and wherein the data codebook is created or updated by referring to previous training images used for pre-training the object detector; extracting specific data codes for testing, respectively corresponding to the reference data codes, from the first data codes for testing to the (n+1)-th data codes for testing; selecting specific test images, corresponding to the specific data codes for testing, among the test images, as rare samples to be used for re-training the object detector; and updating the data codebook by referring to the specific data codes for testing corresponding to the rare samples.