325 citations · 1.2k across the 58 of their papers we have counts for
77 papers
Regularly Truncated M-estimators for Learning with Noisy Labels
Xiaobo Xia, Pengqian Lu, Chen Gong +3
The sample selection approach is very popular in learning with noisy labels. As deep networks learn pattern first, prior methods built on sample selection share a similar training…
Continual Learning From a Stream of APIs
Enneng Yang, Zhenyi Wang, Li Shen +5
Continual learning (CL) aims to learn new tasks without forgetting previous tasks. However, existing CL methods require a large amount of raw data, which is often unavailable due t…
Late Stopping: Avoiding Confidently Learning from Mislabeled Examples
Suqin Yuan, Lei Feng, Tongliang Liu
Sample selection is a prevalent method in learning with noisy labels, where small-loss data are typically considered as correctly labeled data. However, this method may not effecti…
Point-Query Quadtree for Crowd Counting, Localization, and More
Chengxin Liu, Hao Lu, Zhiguo Cao +1
We show that crowd counting can be viewed as a decomposable point querying process. This formulation enables arbitrary points as input and jointly reasons whether the points are cr…
ALIP: Adaptive Language-Image Pre-training with Synthetic Caption
Kaicheng Yang, Jiankang Deng, Xiang An +5
Contrastive Language-Image Pre-training (CLIP) has significantly boosted the performance of various vision-language tasks by scaling up the dataset with image-text pairs collected…
Channel-Wise Contrastive Learning for Learning with Noisy Labels
Hui Kang, Sheng Liu, Huaxi Huang +1
In real-world datasets, noisy labels are pervasive. The challenge of learning with noisy labels (LNL) is to train a classifier that discerns the actual classes from given instances…