Lecture

Image Recognition: Datasets and Algorithms

In course
DEMO: cillum aute est
Non dolore cillum commodo do consectetur tempor ea id consequat dolore. Incididunt qui enim eiusmod dolore. Irure nisi culpa cillum eiusmod ex magna.
Login to see this section
Description

This lecture delves into a 2019 paper proposing a novel approach to image recognition by separating images into shape and appearance components, discussing the datasets used, such as celebrity images, cat head photos, and bird images, and highlighting the challenges and biases in dataset creation. It also explores the impact of large-scale datasets like ImageNet on deep learning models, the importance of ground truths, and the evolution of datasets over time.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related lectures (47)
NFNets: Removing BatchNorm for High-Performance Image Recognition
Explores NFNets as an alternative to BatchNorm in ResNets, achieving high performance on ImageNet.
Machine Learning FundamentalsMOOC: Humanitarian Action in the Digital Age
Covers fundamental principles, opportunities, and challenges in machine learning.
Visual Intelligence: Machines and Minds
Explores visual intelligence, image formation, computer vision, and representation understanding in machines and minds.
Neural Networks: Deep Neural Networks
Explores the basics of neural networks, with a focus on deep neural networks and their architecture and training.
Machine Learning Fundamentals
Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.
Show more

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.