This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Deep Dive: Convolutional Neural Network Algorithms for Specific Challenges CNNs, while powerful, face distinct challenges in their application, particularly in scenarios like datascarcity, overfitting, and unstructured data environments. Making CNN models more interpretable and explainable.
Image Classification Image classification tasks involve CV models categorizing images into user-defined classes for various applications. Based on the presence of a tiger, the entire image is categorized as such. Semantic Segmentation Semantic segmentation aims to identify each pixel within an image for a more detailed categorization.
Also, you can use N-shot learning models to label data samples with unknown classes and feed the new dataset to supervised learning algorithms for better training. The AI community categorizes N-shot approaches into few, one, and zero-shot learning. As the article explains, the N-shot learning paradigms address these data challenges.
These sources can be categorized into three types: textual documents (e.g., KD methods can be categorized into white-box and black-box approaches. In high-stakes decision-making contexts, easily auditable and explainable models are typically favored.
We organize all of the trending information in your field so you don't have to. Join 15,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content