Representation Learning

by Synced 2021-12-06 1

Integrating Self-Attention and Convolution: Tsinghua, Huawei & BAAI’s ACmix Achieves SOTA Performance on CV Tasks With Minimum Cost

In the new paper On the Integration of Self-Attention and Convolution, a research team from Tsinghua University, Huawei Technologies Ltd. and the Beijing Academy of Artificial Intelligence proposes ACmix, a mixed model that leverages the benefits of both self-attention and convolution for computer vision representation tasks while achieving minimum computational overhead compared to its pure convolution or self-attention counterparts.

by Synced 2021-05-07 4

AI Machine Learning & Data Science Research

MIT & IBM ‘Curiosity’ Framework Explores Embodied Environments to Learn Task-Agnostic Visual Representations

A research team from MIT and MIT-IBM Watson AI Lab proposes Curious Representation Learning (CRL), a framework that learns to understand the surrounding environment by training a reinforcement learning (RL) agent to maximize the error of a representation learner to gain an incentive to explore the environment.

by Synced 2021-05-06 3

AI Machine Learning & Data Science Research

Facebook AI Conducts Large-Scale Study on Unsupervised Spatiotemporal Representation Learning

A research team from Facebook AI conducts a large-scale study on unsupervised spatiotemporal representation learning from videos. The work takes a unified perspective on four recent image-based frameworks (MoCo, SimCLR, BYOL, SwAV) and investigates a simple objective that can easily generalize unsupervised representation learning methodologies to space-time.