6 citations · 16 across the 12 of their papers we have counts for
12 papers
Diffusion-Driven Two-Stage Active Learning for Low-Budget Semantic Segmentation
Jeongin Kim, Wonho Bae, YouLee Han +4
Semantic segmentation demands dense pixel-level annotations, which can be prohibitively expensive - especially under extremely constrained labeling budgets. In this paper, we addre…
Uncertainty Herding: One Active Learning Method for All Label Budgets
Wonho Bae, Gabriel L. Oliveira, Danica J. Sutherland
Most active learning research has focused on methods which perform well when many labels are available, but can be dramatically worse than random selection when label budgets are s…
Generalized Coverage for More Robust Low-Budget Active Learning
Wonho Bae, Junhyug Noh, Danica J. Sutherland
The ProbCover method of Yehuda et al. is a well-motivated algorithm for active learning in low-budget regimes, which attempts to "cover" the data distribution with balls of a given…
AdaFlood: Adaptive Flood Regularization
Wonho Bae, Yi Ren, Mohamad Osama Ahmed +3
Although neural networks are conventionally optimized towards zero training loss, it has been recently learned that targeting a non-zero training loss threshold, referred to as a f…
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling
Wonho Bae, Jing Wang, Danica J. Sutherland
Most meta-learning methods assume that the (very small) context set used to establish a new task at test time is passively provided. In some settings, however, it is feasible to ac…
How to prepare your task head for finetuning
Yi Ren, Shangmin Guo, Wonho Bae +1
In deep learning, transferring information from a pretrained network to a downstream task by finetuning has many benefits. The choice of task head plays an important role in fine-t…
Meta Temporal Point Processes
Wonho Bae, Mohamed Osama Ahmed, Frederick Tung +1
A temporal point process (TPP) is a stochastic process where its realization is a sequence of discrete events in time. Recent work in TPPs model the process using a neural network…
Object Discovery via Contrastive Learning for Weakly Supervised Object Detection
Jinhwan Seo, Wonho Bae, Danica J. Sutherland +2
Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations. Current state-of-the-art models benefit…
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels
Mohamad Amin Mohamadi, Wonho Bae, Danica J. Sutherland
We propose a new method for approximating active learning acquisition strategies that are based on retraining with hypothetically-labeled candidate data points. Although this is us…
A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel
Mohamad Amin Mohamadi, Wonho Bae, Danica J. Sutherland
Empirical neural tangent kernels (eNTKs) can provide a good understanding of a given network's representation: they are often far less expensive to compute and applicable more broa…
One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model
Wonho Bae, Junhyug Noh, Milad Jalali Asadabadi +1
Semi-weakly supervised semantic segmentation (SWSSS) aims to train a model to identify objects in images based on a small number of images with pixel-level labels, and many more im…
LID 2020: The Learning from Imperfect Data Challenge Results
Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…