Search Results for author: Kwangrok Ryoo

Found 6 papers, 2 papers with code

SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels

no code implementations20 Nov 2022 Daehwan Kim, Kwangrok Ryoo, Hansang Cho, Seungryong Kim

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.

Learning with noisy labels

3D GAN Inversion with Pose Optimization

1 code implementation13 Oct 2022 Jaehoon Ko, Kyusun Cho, Daewon Choi, Kwangrok Ryoo, Seungryong Kim

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.

3D Reconstruction Image Reconstruction +1

Towards Flexible Inductive Bias via Progressive Reparameterization Scheduling

no code implementations4 Oct 2022 Yunsung Lee, Gyuseong Lee, Kwangrok Ryoo, Hyojun Go, JiHye Park, Seungryong Kim

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.

Inductive Bias Scheduling

ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization

1 code implementation18 Aug 2022 Jiwon Kim, Youngjo Min, Daehwan Kim, Gyuseong Lee, Junyoung Seo, Kwangrok Ryoo, Seungryong Kim

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.

Pseudo Label

Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels

no code implementations CVPR 2022 Jiwon Kim, Kwangrok Ryoo, Junyoung Seo, Gyuseong Lee, Daehwan Kim, Hansang Cho, Seungryong Kim

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.

Data Augmentation Semantic correspondence +1

AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning

no code implementations25 Jan 2022 Jiwon Kim, Kwangrok Ryoo, Gyuseong Lee, Seokju Cho, Junyoung Seo, Daehwan Kim, Hansang Cho, Seungryong Kim

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.

Pseudo Label

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