Postdoc, University of Oxford.

  • Machine Learning: Deep Metric Learning, Robust Representation Learning under Adverse Conditions, e.g., missing labels (semi-supervised learning), noisy labels, sample imbalance, etc.

  • Computer Vision: Image/Video Recognition, Person Re-identification.

  • Something new to come soon!

Hightlight: Robust Learning and Inference under Adverse Conditions, e.g., noisy labels or observations, outliers, adversaries, sample imbalance (long-tailed), etc.

Why important?

DNNs can brute forcelly fit well training examples with random lables (non-meaningful patterns):

In the large-scale training datasets, noisy training data points generally exist. Specifically and explicitly, the observations and their corresponding semantic labels may not matched.

Are deep models robust to massive noise intrinsically?

Intuitive concepts to keep in mind

  • The definition of abnormal examples: A training example, i.e., an observation-label pair, is abnormal when an obserevation and its corresponding annotated label for learning supervision are semantically unmatched.

  • Fitting of abnormal examples: When a deep model fits an abnormal example, i.e., mapping an oberservation to a semantically unmatched label, this abnormal example can be viewed as an successful adversary, i.e., an unrestricted adversarial example.

  • Learning objective: A deep model is supposed to extract/learn meaningful patterns from training data, while avoid fitting any anomaly.


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