Weakly-supervised Learning

173 papers with code • 0 benchmarks • 0 datasets

This task has no description! Would you like to contribute one?


Use these libraries to find Weakly-supervised Learning models and implementations

Most implemented papers

Constrained-CNN losses for weakly supervised segmentation

LIVIAETS/SizeLoss_WSS 12 May 2018

To the best of our knowledge, the method of [Pathak et al., 2015] is the only prior work that addresses deep CNNs with linear constraints in weakly supervised segmentation.

Learning 3D Shape Completion under Weak Supervision

davidstutz/ijcv2018-improved-shape-completion 18 May 2018

We address the problem of 3D shape completion from sparse and noisy point clouds, a fundamental problem in computer vision and robotics.

See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification

wvinzh/WS_DAN_PyTorch 26 Jan 2019

Specifically, for each training image, we first generate attention maps to represent the object's discriminative parts by weakly supervised learning.

Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations

jiwoon-ahn/irn CVPR 2019

For generating the pseudo labels, we first identify confident seed areas of object classes from attention maps of an image classification model, and propagate them to discover the entire instance areas with accurate boundaries.

Open-set Label Noise Can Improve Robustness Against Inherent Label Noise

hongxin001/ODNL NeurIPS 2021

Learning with noisy labels is a practically challenging problem in weakly supervised learning.

Weakly Supervised Deep Detection Networks

hbilen/WSDDN CVPR 2016

Weakly supervised learning of object detection is an important problem in image understanding that still does not have a satisfactory solution.

How does Disagreement Help Generalization against Label Corruption?

xingruiyu/coteaching_plus 14 Jan 2019

Learning with noisy labels is one of the hottest problems in weakly-supervised learning.

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set

Microsoft/Deep3DFaceReconstruction 20 Mar 2019

Recently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency. However, training deep neural networks typically requires a large volume of data, whereas face images with ground-truth 3D face shapes are scarce.

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation

ThoroughImages/CAMEL ICCV 2019

In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image-level labels.