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.
AUTHORS
Written by
Sean BellYiqun Liu
Sami Alsheikh
Yina Tang
Ed Pizzi
M. Henning
Karun Singh
Omkar Parkhi
Fedor Borisyuk
Publisher
KDD
Recent Publications
May 03, 2021
NLP
Support-Set bottlenecks for video-text representation learning
The dominant paradigm for learning video-text representations -- noise contrastive learning -- increases the similarity of the representations of pairs of samples
Mandela Patrick, Po-Yao Huang, Florian Metze , Andrea Vedaldi, Alexander Hauptmann, Yuki M. Asano, João Henriques
May 03, 2021
April 08, 2021
RESPONSIBLE AI
INTEGRITY
Towards measuring fairness in AI: the Casual Conversations dataset
This paper introduces a novel dataset to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of age, genders, apparent skin tones and ambient lighting conditions
Caner Hazirbas, Joanna Bitton,Brian Dolhansky, Jacqueline Pan, Albert Gordo, Cristian Canton Ferrer
April 08, 2021
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
March 11, 2021
Fairness On The Ground: Applying Algorithmic Fairness Approaches To Production Systems
This paper presents an example of one team’s approach to the challenge of applying algorithmic fairness approaches to complex production systems within the context of a large technology company.
Chloé Bakalar, Renata Barreto, Stevie Bergman, Miranda Bogen, Bobbie Chern, Sam Corbett-Davies, Melissa Hall, Isabel Kloumann, Michelle Lam, Joaquin Quiñonero Candela, Manish Raghavan, Joshua Simons, Jonathan Tannen, Edmund Tong, Kate Vredenburgh, Jiejing Zhao
March 11, 2021
