all AI news
One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching. (arXiv:2205.15721v1 [cs.CV])
June 1, 2022, 1:12 a.m. | Khoa D. Doan, Peng Yang, Ping Li
cs.CV updates on arXiv.org arxiv.org
Image hashing is a principled approximate nearest neighbor approach to find
similar items to a query in a large collection of images. Hashing aims to learn
a binary-output function that maps an image to a binary vector. For optimal
retrieval performance, producing balanced hash codes with low-quantization
error to bridge the gap between the learning stage's continuous relaxation and
the inference stage's discrete quantization is important. However, in the
existing deep supervised hashing methods, coding balance and low-quantization
error are …
More from arxiv.org / cs.CV updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Analytics Engineer
@ CircleCI | Remote (US), Remote (Canada), San Francisco, Denver
Bilingual Executive Assistant/Data Analyst - (French and English) - Export
@ Dangote Group | Lagos, Lagos, Nigeria
Workday Services Data Lead
@ WPP | Mexico City, Mexico
Business Data Analyst
@ Nordea | Tallinn, EE, 11415
Data Integrity Lead
@ BioNTech SE | Gaithersburg, MD, US, MD 20878