I’ve learned about S3D(Separable 3D CNN) today so I like to share it here. S3D helps solve three challenges for video analysis. How to understand spatial information. (Recognizing the appearance of an object) How to understand temporal information. (Such as…
“Local” means only understanding the CURRENT “time” and “space”. To understand “non-local” nuances (What will the person in the image do next? Where will the soccer ball being kicked head towards?), if we were to use traditional methods such as…
I found these losses quite hard to distinguish between each other. So, I created an intuitive explanation of the differences.
What is BERT? BERT is a deep learning architecture for natural language processing. If you stack the Transformer’s encoder, you get BERT. What can BERT Solve? Neural Machine Translation Question Answering Sentiment Analysis Text Summarization How to solve the problems…
You can use 3DSlicer to visualize 3d medical data. Note that you need to convert your data to a volume(nifti file) if your current data is a single section(such as dcm file).
Discriminative models predict using conditional probability which makes them more robust to outliers. On the other hand, generative models tries to learn the distribution. This can be useful when you want to generate new data.
Spatial Pyramid Pooling helps the network output the same shape regardless of any aspect ratio and input size. Instead of Pooling with a fixed filter size, it divides the input with different levels of ratio, so the output would not…
Autoencoders encode an input to a smaller representation vector (also called a latent vector) and decode that to restore the original input. For example, you can encode an image, send the encoded image to someone, and have them decode it…
Vision Transformers is inspired by Transformers for natural language processing. Unlike traditional convolution networks with pyramid architectures, ViT has an isotropic architecture, where the input does not downsize. The steps are the following. Split images into “patches” Flatten each path…
There are mainly 2 types of architectures in computer vision. Pyramid Architecture The size/shape of the element is reduced: Ex. Traditional Convolution Networks Isotropic Architecture Have equal size and shape for all elements throughout the network: Ex. Transformers Recent research…