ImageNet | Synced

by Synced 2022-04-14 0

Alibaba’s USI: A Unified Scheme for Training Any Backbone on ImageNet That Delivers Top Results Without Hyperparameter Tuning

In the new paper Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results, a research team from Alibaba Group’s DAMO Academy introduces USI (Unified Scheme for ImageNet), a unified scheme for training any backbone on ImageNet that does not require adjustments or hyperparameter tuning between different models, and consistently yields top model results in terms of accuracy and efficiency.

by Synced 2022-01-17 2

AI Computer Vision & Graphics Machine Learning & Data Science Popular Research

Pushing the Limits of Self-Supervised ResNets: DeepMind’s ReLICv2 Beats Strong Supervised Baselines on ImageNet

A DeepMind research team proposes ReLICv2, which demonstrates for the first time that representations learned without labels can consistently outperform a strong, supervised baseline on ImageNet and even achieve comparable results to state-of-the-art self-supervised vision transformers (ViTs).

by Synced 2021-06-02 1

AI Computer Vision & Graphics Machine Learning & Data Science Research

Google & Rutgers’ Aggregating Nested Transformers Yield Better Accuracy, Data Efficiency and Convergence

A research team from Google Cloud AI, Google Research and Rutgers University simplifies vision transformers’ complex design, proposing nested transformers (NesT) that simply stack basic transformer layers to process non-overlapping image blocks individually. The approach achieves superior ImageNet classification accuracy and improves model training efficiency.

by Synced 2021-02-16 3

AI Machine Learning & Data Science Research

DeepMind Achieves High-Performance Large-Scale Image Recognition Without Batch Normalization

DeepMind has designed a family of Normalizer-Free ResNets (NFNets) that can be trained in larger batch sizes and stronger data augmentations and have set new SOTA validation accuracies on ImageNet.

by Synced 2021-01-25 4

AI Machine Learning & Data Science Research

AI Art Critic? New Dataset and Models Make Emotional Sense of Visual Artworks

Is it possible for machines to understand and integrate human emotions in visual arts? Meet ArtEmis, a new large-scale dataset of emotional reactions and explanations for visual artworks.

by Synced 2020-07-23 1

AI Machine Learning & Data Science Research

Microsoft & MIT Apply Adversarially Robust Models for Better Transfer Learning

Despite being less accurate on ImageNet, adversarially robust neural networks can match or improve their standard counterparts’ transfer performance.

by Synced 2020-06-23 5

AI Machine Learning & Data Science Research

Google & DeepMind Researchers Revamp ImageNet

Google Brain in Zürich and DeepMind London researchers believe one of the world’s most popular image databases may need a makeover.

by Synced 2020-02-23 3

AI AI Weekly Global Research

NeurIPS 2020 Changes Review Process; Hinton & Google Brain Unsupervised Model Boosts ImageNet Accuracy 7%; DeepMind Releases New JAX Libraries

Synced Global AI Weekly February 23rd

by Synced 2020-02-19 9

AI Computer Vision & Graphics Machine Learning & Data Science Research

Geoffrey Hinton & Google Brain Unsupervised Learning Algorithm Improves SOTA Accuracy on ImageNet by 7%

Researchers have proposed a simple but powerful “SimCLR” framework for contrastive learning of visual representations.

by Synced 2020-02-16 3

AI AI Weekly Global

AAAI 2020 Best Papers; Turing Award Winners See a Turning Point for Deep Learning; MIT Reveals Voting App Vulnerabilities

Synced Global AI Weekly February 16th

by Synced 2020-02-13 1

AI Computer Vision & Graphics Machine Learning & Data Science Research

Google Brain & CMU Semi-Supervised ‘Noisy Student’ Achieves 88.4% Top-1 Accuracy on ImageNet

Researchers from Google Brain and Carnegie Mellon University have released models trained with a semi-supervised learning method called “Noisy Student” that achieve 88.4 percent top-1 accuracy on ImageNet.

by Synced 2019-07-22 0

AI Research

Natural Adversarial Examples Slash Image Classification Accuracy by 90%

Researchers from UC Berkeley and the Universities of Washington and Chicago have released a set of natural adversarial examples, which they call “ImageNet-A.”

by Synced 2019-06-26 1

AI Research

Facebook Model Pretrained on Billions of Instagram Hashtags Achieves SOTA Results on Top-1 ImageNet

Facebook AI research team show how they trained a large convolutional network to predict hashtags on some 3.5 billion social media images. The research returned a state-of-the-art top-1 accuracy result of 85.4 percent on ImageNet.

by Synced 2019-06-11 0

AI Research

New DeepMind Unsupervised Image Model Challenges AlexNet

A new paper from Google’s UK-based research company DeepMind addresses this with a model based on Contrastive Predictive Coding (CPC) that outperforms the fully-supervised AlexNet model in Top-1 and Top-5 accuracy on ImageNet.

by Synced 2019-04-08 0

AI Research

Megvii Introduces Single-Path, One-Shot Method for NAS Design

Chinese AI unicorn Megvii Technology has proposed a new single-path, one-shot NAS design approach which makes various applications more convenient and achieves start-of-the-art performance on the large dataset ImageNet.

by Synced 2019-02-25 0

AI Research

SenseTime Trains ImageNet/AlexNet In Record 1.5 minutes

Researchers from Beijing-based AI unicorn SenseTime and Nanyang Technological University have trained ImageNet/AlexNet in a record-breaking 1.5 minutes, a significant 2.6 times speedup over the previous record of 4 minutes.

by Synced 2018-11-26 0

AI Research

FAIR Paper Questions Pre-Training’s Efficacy in CV

ImageNet Pre-training is common in a variety of CV (Computer Vision) tasks, as something of a consensus has emerged that pre-training can help a model learn transferrable information that can be useful for target tasks.

by Synced 2018-11-25 0

AI AI Weekly

Global Companies Are Making AI Breakthroughs

Synced Global AI Weekly November 25th

by Synced 2018-11-19 0

AI Asia Global News Research

Sony Breaks ResNet-50 Training Record on ImageNet

Researchers from Japanese electronics giant Sony have trained the ResNet-50 neural network model on ImageNet in a record-breaking 224 seconds — 43.4 percent better than the previous fastest time for the benchmark task.

by Synced 2018-11-13 2

AI Conference

OpenAI Founder: Short-Term AGI Is a Serious Possibility

Artificial general intelligence (AGI) is the long-range, human-intelligence-level target of contemporary AI researchers worldwide. It’s believed AGI has the potential to meet basic human needs globally, end poverty, cure diseases, extend life, and even mitigate climate change. In short, AGI is the tech that could not only save the world, but build a utopia.

by Synced 2018-07-31 0

AI China Research

Tencent ML Team Trains ImageNet In Record Four Minutes

Since 2010, the annual ImageNet Large-Scale Visual Recognition Challenge has been the most widely recognized benchmark for testing image recognition algorithms. Tencent Machine Learning picks up the challenge with its new paper Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes.

by Synced 2017-08-23 1

Research

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

The ShuffleNet utilizes pointwise group convolution and channel shuffle to reduce computation cost while maintaining accuracy. It manages to obtain lower top-1 error than the MobileNet system on ImageNet classification, and achieves ~13x actual speedup over AlexNet while maintaining comparable accuracy.

by Synced 2017-08-21 0

Conference Research

CVPR2017 Honorable Mention Paper – Annotating Object Instances with a Polygon-RNN

This paper proposes not only a method as a useful tool but also a new concept for modeling complex annotation as a simple polygon.

by Synced 2017-06-30 0

Research

Understanding the Mechanisms of Deep Transfer Learning for Medical Images

This paper systematically studies how a convolutional neural network, trained on ImageNet for image classification tasks, works on medical images – or more precisely on ultrasound images – for the “Kidney Detection” problem.

by Synced 2017-04-30 2

China Community Industry

The Humans Behind Artificial Intelligence

AI companies require accurate data for specialized applications, and seemingly little things such as labelling and tagging demand accuracy. At present, only humans are up for the task.

by Synced 2017-04-16 2

Research

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

This paper proposed a computationally efficient convolutional layer to upscale the final low-resolution feature map to a high-resolution output.