Search Results for author:
Found 7 papers, 3 papers with code
Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection
For object detection task with noisy labels, it is important to consider not only categorization noise, as in image classification, but also localization noise, missing annotations, and bogus bounding boxes.
SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels
To address this, some methods were proposed to automatically split clean and noisy labels, and learn a semi-supervised learner in a Learning with Noisy Labels (LNL) framework.
3D GAN Inversion with Pose Optimization
With the recent advances in NeRF-based 3D aware GANs quality, projecting an image into the latent space of these 3D-aware GANs has a natural advantage over 2D GAN inversion: not only does it allow multi-view consistent editing of the projected image, but it also enables 3D reconstruction and novel view synthesis when given only a single image.
Towards Flexible Inductive Bias via Progressive Reparameterization Scheduling
In addition, through Fourier analysis of feature maps, the model's response patterns according to signal frequency changes, we observe which inductive bias is advantageous for each data scale.
ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization
We present a novel semi-supervised learning framework that intelligently leverages the consistency regularization between the model's predictions from two strongly-augmented views of an image, weighted by a confidence of pseudo-label, dubbed ConMatch.
Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels
In this paper, we present a simple, but effective solution for semantic correspondence that learns the networks in a semi-supervised manner by supplementing few ground-truth correspondences via utilization of a large amount of confident correspondences as pseudo-labels, called SemiMatch.
AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning
In this paper, we address this limitation with a novel SSL framework for aggregating pseudo labels, called AggMatch, which refines initial pseudo labels by using different confident instances.
