In this page, I will present several topics in Computer Vision that I considered as important tasks. Each topic gives my insights towards it. If you think it is really interesting and want to discuss with me, please contact me without hesitation.

# Classic Tasks in Computer Vision.

## Interpretable models/XAI: how black box works?

As we know, neural networks are not as simple as linear regression, and complex, deep layers and skip connections make the structure confusing (unlike the decision tree, the partitions on each node are interpretable). Before the gradient-descent based neural network, a semantic convolution kernel, such as the Sobel operator and the Robert cross-gradient operator, are manually set for specific tasks (edge detection). During the era of these operators, people are hand-designing these kernels with wisdom. When the convolutional neural network came out, these multi-layered convolution kernels of self-learning became an unsolved mystery. What actually did they learn?

There are many work on interpretable deep neural networks, such as Professor Bolei Zhou interpretability for a network defined as the number of interpretable kernels. What is an interpretable kernel? Bolei found that after the activated layers, the extracted feature map has the characteristics of high activation value in specific semantics. For example, the feature map of the last layer of VGG-19 has peaks on specific objects such as the dog’s ears and nose. What is funny is that, the kernel which highly activate in the dog’s ears also activate in the cat’s ears(they are in similar shapes and textures). This might be the knowledge that the VGG-19 learned.

Another very interesting work about interpretable models is Professor Songchun Zhu’s architecture with graph embedded, aiming to design a structure with interpretable models. My past-supervisor Xuming He also very interested in this topic, his idea is to distill network knowledge into a interpretable model via Teacher-Student network method.

My Idea: Maybe there is some distribution deep into the kernels or the activated map. I am going to use unsupervised learning method to analysis the distribution. Details under editing.

## Generative models: detailed wins!

under construction

under construction