CNN is a neural network that includes convolutional processing and is a well-known technology applied to the field of image processing. Choose the most inappropriate sentence regarding CNN below.

1: Convolutional processing is a technique for extracting features in an image using kernels (filters), and before CNN, convolutional processing used artificially designed kernels. Before CNNs, convolutional processing used artificially designed kernels. However, convolutional processing in CNNs can extract features with higher accuracy by learning numerical values in the kernels through back-propagation.
2: Pooling is a method for compressing the input size of images and feature maps. This process has the effect of being invariant to slight differences in the color and tone of objects in the image without requiring parameters to be learned.
3: In the 2014 paper in which VGG was proposed, it was shown that by repeating the convolution process of only 3x3 kernels many times, it is possible to cover the same receptive field as a large kernel size while keeping the number of parameters low.
4: A technique that solves the "gradient loss problem due to multilayering," which is a problem faced by VGG, is the skip structure (jump structure, skip connection) proposed in the 2015 ResNet paper. ResNet has achieved a super multi-layered structure with 152 layers by learning the difference between the input and output of each Residual Block, which is a component of ResNet.