RESEARCH

ML APPLICATIONS

GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce

August 22, 2020

Abstract

In this paper, we present GrokNet, a deployed image recognition system for commerce applications. GrokNet leverages a multi-task learning approach to train a single computer vision trunk. We achieve a 2.1x improvement in exact product match accuracy when compared to the previous state-of-the-art Facebook product recognition system. We achieve this by training on 7 datasets across several commerce verticals, using 80 categorical loss functions and 3 embedding losses. We share our experience of combining diverse sources with wide-ranging label semantics and image statistics, including learning from human annotations, user-generated tags, and noisy search engine interaction data. GrokNet has demonstrated gains in production applications and operates at Facebook scale.

Download the Paper

AUTHORS

Written by

Sean Bell

Yiqun Liu

Sami Alsheikh

Yina Tang

Ed Pizzi

M. Henning

Karun Singh

Omkar Parkhi

Fedor Borisyuk

Publisher

KDD

Recent Publications

March 13, 2021

REINFORCEMENT LEARNING

On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning

Model-based Reinforcement Learning (MBRL) is a promising framework for learning control in a data-efficient manner…

Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan Lambert, Andre Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra

March 13, 2021

February 27, 2021

RANKING & RECOMMENDATIONS

SYSTEMS RESEARCH

Understanding Training Efficiency of Deep Learning Recommendation Models at Scale

The use of GPUs has proliferated for machine learning workflows and is now considered mainstream for many deep learning models…

Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, Kim Hazelwood

February 27, 2021

February 01, 2021

RANKING & RECOMMENDATIONS

Anytime Inference with Distilled Hierarchical Neural Ensembles

Inference in deep neural networks can be computationally expensive, and networks capable of anytime inference are important in scenarios where the amount of compute or quantity of input data varies over time.…

Adria Ruiz, Jakob Verbeek

February 01, 2021

January 09, 2021

COMPUTER VISION

Tarsier: Evolving Noise Injection in Super-Resolution GANs

Super-resolution aims at increasing the resolution and level of detail within an image.…

Baptiste Roziere, Nathanaël Carraz Rakotonirina, Vlad Hosu, Andry Rasoanaivo, Hanhe Lin, Camille Couprie, Olivier Teytaud

January 09, 2021

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.