I really wonder about data preprocessing is really necessary and important in deep learning.
It's really hard to say clearly about difference between Machine learning and deep learning. In definetion, The difference is whether or not humans intervene in the process of finding features. Machine learning need humans intervene to do feature extraction, so must need data preprocessing. So we do PCA, denoising, data balancing, and so on.
Machine learning is important to pre-processing. But, In deep learning, Is really important to do preprocessing at data? I'm not asking about such as embedding in NLP. text embedding is necessary for transform text to computer recognizable.
I'm really confused while studying about deep-learning. Someone says that it's important to do pre-processing, and someone says that isn't important to do pre-processing. For example, I'll show some situation about computer vision. Let's think about Denoising, erase such as pepper and salt noise in image. Is it important to do denoise for input image? someone who agree about to do preprocessing says that noise is unexpected pixel value, so if we denoised noise, than we can get more clear image, so It brings more good performance. someone who disagree about to do preprocessing says 2 reasons.
First, If we can get 100% clear original image without any noise, then compair denoised image and clear original image. It's not totally same image. Also, we can't get without any noise image because of physical reasons.
Second, Deep learning shows better performance when the amount of input data is large. If human intervene to feature extraction, It's not efficient to do with human power. Deep learning can do feature extraction itself, don't need human to feature extraction.
I apolized that I think it's not good example about my question. so, I give another example. Think about Iris dataset. In Machine learning, We do PCA(or else) and select efficient data(ex: sepal and petal width and length data) and input data to model. But, deep learning don't need select efficient data. If we have nice deep learning model, then we can input whole data.
last example, It is estimated that GPT-3 has been learned with more than 300 billion sentences. I don't think training dataset did pre-processing. Because, sentence might have typo, wrong grammer, slang, abbreviation, etc because human writing. If do preprocessing about typo, wrong grammer, etc then It would have consumed a lot of human labor and costs.
So... Is deep learning really need pre-processing?