Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟮𝟭 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀 𝗣𝗿𝗲𝘀𝗲𝗻𝘁 𝗧𝗵𝗲 ‘𝗢𝗻𝗲 𝗣𝗮𝘀𝘀 𝗜𝗺𝗮𝗴𝗲𝗡𝗲𝘁’ (𝗢𝗣𝗜𝗡) 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗧𝗼 𝗦𝘁𝘂𝗱𝘆 𝗧𝗵𝗲 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗻𝗲𝘀𝘀 𝗢𝗳 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗜𝗻 𝗔 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗦𝗲𝘁𝘁𝗶𝗻𝗴 Follow me for a similar post: 🇮🇳 Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗢𝗻𝗲 𝗣𝗮𝘀𝘀 𝗜𝗺𝗮𝗴𝗲𝗡𝗲𝘁 🔸 This paper is published in NeurIPS 2021. 🔸 The ImageNet database, which was first introduced at the Conference of Computer Vision and Pattern Recognition in 2009 and today contains over 14 million tagged images, has become one of the most prominent standards in the field of computer vision. ImageNet is also a static dataset, but real-world data is frequently streamed and on a considerably more extensive scale. While academics are constantly working to increase model accuracy on ImageNet, there has been minimal focus on improving resource efficiency in ImageNet supervised learning. 🔸 Researchers from DeepMind present the One Pass ImageNet (OPIN) problem, designed to study and understand deep learning in a streaming setting with constrained data storage, with the intent of developing systems that can train a model with each example passed once through the system. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 We present the One Pass ImageNet (OPIN) problem, which aims to study the effectiveness of deep learning in a streaming setting. ImageNet is a widely known benchmark dataset that has helped drive and evaluates recent advancements in deep learning.  🔸 Typically, deep learning methods are trained on static data that the models have random access to, using multiple passes over the dataset with a random shuffle at each epoch of training. Such data access assumption does not hold in many real-world scenarios where massive data is collected from a stream and storing and accessing all the data becomes impractical due to storage costs and privacy concerns.  🔸 For OPIN, we treat the ImageNet data as arriving sequentially, and there is a limited memory budget to store a small subset of the data. We observe that training a deep network in a single pass with the same training settings used for multi-epoch training results in a huge drop in prediction accuracy.  🔸 We show that the performance gap can be significantly decreased by paying a small memory cost and utilizing techniques developed for continual learning, despite the fact that OPIN differs from typical continual problem settings. We propose using OPIN to study resource-efficient deep learning. ------------------------------------------------------------------- #computervision #artificialintelligence #innovation -------------------------------------------------------------------