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Yanbei Chen

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2 papers

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NeurIPS Conference 2021 Conference Paper

Robustness via Uncertainty-aware Cycle Consistency

Uddeshya Upadhyay
Yanbei Chen
Zeynep Akata

Unpaired image-to-image translation refers to learning inter-image-domain mapping without corresponding image pairs. Existing methods learn deterministic mappings without explicitly modelling the robustness to outliers or predictive uncertainty, leading to performance degradation when encountering unseen perturbations at test time. To address this, we propose a novel probabilistic method based on Uncertainty-aware Generalized Adaptive Cycle Consistency (UGAC), which models the per-pixel residual by generalized Gaussian distribution, capable of modelling heavy-tailed distributions. We compare our model with a wide variety of state-of-the-art methods on various challenging tasks including unpaired image translation of natural images spanning autonomous driving, maps, facades, and also in the medical imaging domain consisting of MRI. Experimental results demonstrate that our method exhibits stronger robustness towards unseen perturbations in test data. Code is released here: https: //github. com/ExplainableML/UncertaintyAwareCycleConsistency.

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AAAI Conference 2020 Conference Paper

Semi-Supervised Learning under Class Distribution Mismatch

Yanbei Chen
Xiatian Zhu
Wei Li
Shaogang Gong

Semi-supervised learning (SSL) aims to avoid the need for collecting prohibitively expensive labelled training data. Whilst demonstrating impressive performance boost, existing SSL methods artiﬁcially assume that small labelled data and large unlabelled data are drawn from the same class distribution. In a more realistic scenario with class distribution mismatch between the two sets, they often suffer severe performance degradation due to error propagation introduced by irrelevant unlabelled samples. Our work addresses this under-studied and realistic SSL problem by a novel algorithm named Uncertainty- Aware Self-Distillation (UASD). Speciﬁcally, UASD produces soft targets that avoid catastrophic error propagation, and empower learning effectively from unconstrained unlabelled data with out-of-distribution (OOD) samples. This is based on joint Self-Distillation and OOD ﬁltering in a uniﬁed formulation. Without bells and whistles, UASD signiﬁcantly outperforms six state-of-the-art methods in more realistic SSL under class distribution mismatch on three popular image classiﬁcation datasets: CIFAR10, CIFAR100, and TinyImageNet.

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