Pseudo Label Filtering

10 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes

RuijieZhu94/EC-Depth 12 Oct 2023

Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics.

Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering

gorilla-lab-scut/ttac 6 Jun 2022

Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available and instant inference on target domain is required.

Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level prototype memory

RS-CSU/MemoryAdaptNet-master 16 Aug 2022

This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery.

Pseudo-Label Noise Suppression Techniques for Semi-Supervised Semantic Segmentation

christmasfan/ssl_denoising_segmentation 19 Oct 2022

Current SSL approaches use an initially supervised trained model to generate predictions for unlabelled images, called pseudo-labels, which are subsequently used for training a new model from scratch.

Probabilistic Domain Adaptation for Biomedical Image Segmentation

computational-cell-analytics/probabilistic-domain-adaptation 21 Mar 2023

We further study joint and separate source-target training strategies and evaluate our method on three challenging domain adaptation tasks for biomedical segmentation.

Semi-Supervised Semantic Segmentation With Region Relevance

nust-machine-intelligence-laboratory/torchsemiseg2 23 Apr 2023

The most common approach is to generate pseudo-labels for unlabeled images to augment the training data.

FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images

snehaputul/FishSegSSL Journal of Imaging 2024

In this study, we explore and benchmark two popular semi-supervised methods from the perspective image domain for fish-eye image segmentation.

Uncertainty-Aware Pseudo-Label Filtering for Source-Free Unsupervised Domain Adaptation

chenxi52/upa 17 Mar 2024

Source-free unsupervised domain adaptation (SFUDA) aims to enable the utilization of a pre-trained source model in an unlabeled target domain without access to source data.

Less is More: Pseudo-Label Filtering for Continual Test-Time Adaptation

tjy1423317192/PLF 3 Jun 2024

Continual Test-Time Adaptation (CTTA) aims to adapt a pre-trained model to a sequence of target domains during the test phase without accessing the source data.

PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation

axi404/pmt 8 Sep 2024

In this paper, we propose a simple yet effective semi-supervised learning framework, termed Progressive Mean Teachers (PMT), for medical image segmentation, whose goal is to generate high-fidelity pseudo labels by learning robust and diverse features in the training process.