454 citations · 5.5k across the 247 of their papers we have counts for
11 papers · 1 filter
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training
Ming-Kun Xie, Jia-Hao Xiao, Pei Peng +3
The key to multi-label image classification (MLC) is to improve model performance by leveraging label correlations. Unfortunately, it has been shown that overemphasizing co-occurre…
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images
Jialiang Tang, Shuo Chen, Gang Niu +2
Knowledge distillation aims to learn a lightweight student network from a pre-trained teacher network. In practice, existing knowledge distillation methods are usually infeasible w…
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks
Salah Ghamizi, Jingfeng Zhang, Maxime Cordy +3
While leveraging additional training data is well established to improve adversarial robustness, it incurs the unavoidable cost of data collection and the heavy computation to trai…
Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics
Masato Ishii, Masashi Sugiyama
In this paper, we propose a novel domain adaptation method for the source-free setting. In this setting, we cannot access source data during adaptation, while unlabeled target data…
Do Public Datasets Assure Unbiased Comparisons for Registration Evaluation?
Jie Luo, Guangshen Ma, Sarah Frisken +8
With the increasing availability of new image registration approaches, an unbiased evaluation is becoming more needed so that clinicians can choose the most suitable approaches for…
Are Registration Uncertainty and Error Monotonically Associated
Jie Luo, Sarah Frisken, Duo Wang +3
In image-guided neurosurgery, current commercial systems usually provide only rigid registration, partly because it is harder to predict, validate and understand non-rigid registra…