Postdoc, University of Oxford

  • Google Scholar
  • 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.

  • Academic Reviewer: TPAMI, TNNLS, Knowledge Based Systems, AAAI, etc

  • 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):

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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