47 citations · 50 across the 3 of their papers we have counts for
6 papers · 1 filter
Few-shot Fine-tuning is All You Need for Source-free Domain Adaptation
Suho Lee, Seungwon Seo, Jihyo Kim +2
Recently, source-free unsupervised domain adaptation (SFUDA) has emerged as a more practical and feasible approach compared to unsupervised domain adaptation (UDA) which assumes th…
Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning
Jae-Hun Lee, Doyoung Yoon, ByeongMoon Ji +2
Linear probing (LP) (and -NN) on the upstream dataset with labels (e.g., ImageNet) and transfer learning (TL) to various downstream datasets are commonly employed to evaluate th…
Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions
Jihyo Kim, Jeonghyeon Kim, Sangheum Hwang
Active learning aims to identify the most informative data from an unlabeled data pool that enables a model to reach the desired accuracy rapidly. This benefits especially deep neu…
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
Mitko Veta, Yujing J. Heng, Nikolas Stathonikos +30
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is highly subjective an…
Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks
Sangheum Hwang, Sunggyun Park
We introduce an accurate lung segmentation model for chest radiographs based on deep convolutional neural networks. Our model is based on atrous convolutional layers to increase th…
Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation
Hyo-Eun Kim, Sangheum Hwang
A weakly-supervised semantic segmentation framework with a tied deconvolutional neural network is presented. Each deconvolution layer in the framework consists of unpooling and dec…