Self-driving Image Segmentation
Design a self-driving car system focusing on its perception component (semantic image segmentation in particular)
Given a text and knowledge base, find all the entity mentions in the text(Recognize) and then link them to the corresponding correct entry in the knowledge base(Disambiguate).
VS Property Configuration C/C++
For my own projects, the project should be set up as the following:
Display media recommendations for a specific user. Type of user feedback includes explicit and implicit feedbacks. The implicit feedback allows collecting a large amount of training data.
Design a Twitter with 500 million daily active users feed system that will show the most relevant tweets for a user based on their social graph.
Ask for questions: Scale, Scope, Personalization.
The Grad-CAM (Gradient-weighted Class Activation Mapping) is a generalization of CAM and is applicable to a significantly broader range of CNN model families.
The intuition is to expect the last convolutional layers to have the best compromise between high-level semantics and detailed spatial information which is lost in fully-connected layers. The neurons in these layers look for semantic class-specific information in the image.
$$L_{Grad-CAM}^c = ReLU(\sum_k\alpha_k^cA^k)$$
where $$\alpha_k^c = \frac{1}{Z}\sum_i\sum_j\frac{\partial{y_c}}{\partial{A_{ij}^k}}$$