As a consequence, we require significantly more compute in order to produce features competitive with those from top unsupervised convolutional nets.
New arXiv papers on Mondays over the past few years… pic.twitter.com/I0lA3PrSUt — Denny Britz (@dennybritz) May 19, 2020 Deep Learning has produced some amazing results in Image Recognition, NLP, generative models, games, and more.
Considering how to apply these notions in practice to improve zero-shot learning performance, we also introduce Class-Matching DIM (CMDIM), a variant of the popular unsupervised learning algorithm Deep InfoMax, which results in very strong performance compared to a wide range of baselines.
By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots.
At this year’s CVPR conference, Facebook AI is pushing the state of the art forward in many important areas of CV, including core segmentation tasks, architecture search, transfer learning, and multimodal learning.