Tag: self-supervised learning

AI Machine Learning & Data Science Research

Microsoft’s Self-Supervised Bug Detection and Repair Approach Betters Baselines By Up to 30%

In the NeurIPS 2021-accepted paper Self-Supervised Bug Detection and Repair, a Microsoft Research team proposes BUGLAB, a self-supervised approach that significantly improves on baseline methods for detecting bugs in real-life code.

AI Computer Vision & Graphics Machine Learning & Data Science Research

Facebook AI & JHU’s MaskFeat Method Surpasses Kaiming He’s MAE, Sets New SOTA in Video Action Recognition

In the new paper Masked Feature Prediction for Self-Supervised Visual Pre-Training, a Facebook AI Research and Johns Hopkins University team presents a novel Masked Feature Prediction (MaskFeat) approach for the self-supervised pretraining of video models that achieves SOTA results on video benchmarks.

AI Machine Learning & Data Science Research

UC Berkeley’s Sergey Levine Says Combining Self-Supervised and Offline RL Could Enable Algorithms That Understand the World Through Actions

In the new paper Understanding the World Through Action, UC Berkeley assistant professor in the department of electrical engineering and computer sciences Sergey Levine argues that a general, principled, and powerful framework for utilizing unlabelled data can be derived from reinforcement learning to enable machine learning systems leveraging large datasets to understand the real world.

AI Computer Vision & Graphics Machine Learning & Data Science Research

Apple Study Reveals the Learned Visual Representation Similarities and Dissimilarities Between Self-Supervised and Supervised Methods

An Apple research team performs a comparative analysis on a contrastive self-supervised learning (SSL) algorithm (SimCLR) and a supervised learning (SL) approach for simple image data in a common architecture, shedding light on the similarities and dissimilarities in their learned visual representation patterns.