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Yifan Ding

10 papers hereh-index 151.5k citations26 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author6
  • middle author3
  • last author1

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.CL2
  • cs.DC1
  • cs.LG1
  • cs.SD1
  • eess.AS1
same name
  • Yifan Ding — 9 papers, h 6
  • Yifan Ding — 8 papers, h 5
  • Yifan Ding — 6 papers, h 6
  • Yifan Ding — 4 papers, h 3
  • Yifan Ding — 3 papers, h 5
  • Yifan Ding — 3 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182022
most citedReddit Entity Linking Dataset

14 citations · 39 across the 7 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2019

Defending Against Adversarial Attacks Using Random Forests

Yifan Ding, Liqiang Wang, Huan Zhang +3

As deep neural networks (DNNs) have become increasingly important and popular, the robustness of DNNs is the key to the safety of both the Internet and the physical world. Unfortun…

cs.CV2019★ 14 cited

Frame-Recurrent Video Inpainting by Robust Optical Flow Inference

Yifan Ding, Chuan Wang, Haibin Huang +3

In this paper, we present a new inpainting framework for recovering missing regions of video frames. Compared with image inpainting, performing this task on video presents new chal…

cs.CV2019

Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation

Jiaming Liu, Chi-Hao Wu, Yuzhi Wang +8

In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising. Compared with traditional RGB image denoising, performing this task…

cs.CV2018

A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels

Yifan Ding, Liqiang Wang, Deliang Fan +1

The recent success of deep neural networks is powered in part by large-scale well-labeled training data. However, it is a daunting task to laboriously annotate an ImageNet-like dat…

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