4 citations · 7 across the 15 of their papers we have counts for
6 papers · 1 filter
Depth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material Classification
Prakash Chandra Chhipa, Richa Upadhyay, Rajkumar Saini +4
This work presents a novel self-supervised representation learning method to learn efficient representations without labels on images from a 3DPM sensor (3-Dimensional Particle Mea…
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data
Takeaki Kadota, Hideaki Hayashi, Ryoma Bise +2
Automatic image-based disease severity estimation generally uses discrete (i.e., quantized) severity labels. Annotating discrete labels is often difficult due to the images with am…
MontageGAN: Generation and Assembly of Multiple Components by GANs
Chean Fei Shee, Seiichi Uchida
A multi-layer image is more valuable than a single-layer image from a graphic designer's perspective. However, most of the proposed image generation methods so far focus on single-…
Revealing Reliable Signatures by Learning Top-Rank Pairs
Xiaotong Ji, Yan Zheng, Daiki Suehiro +1
Signature verification, as a crucial practical documentation analysis task, has been continuously studied by researchers in machine learning and pattern recognition fields. In spec…
Font Generation with Missing Impression Labels
Seiya Matsuda, Akisato Kimura, Seiichi Uchida
Our goal is to generate fonts with specific impressions, by training a generative adversarial network with a font dataset with impression labels. The main difficulty is that font i…
Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images
Prakash Chandra Chhipa, Richa Upadhyay, Gustav Grund Pihlgren +3
This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors. O…