- A former postdoc of 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
- I am working on AI for synthetic biology now, which is exciting and has huge potential
Featured Research Delivering Permalink
Hightlight: Robust Learning and Inference under Adverse Conditions, e.g., noisy labels or observations, outliers, adversaries, sample imbalance (long-tailed), etc. Permalink
Why important? DNNs can brute forcelly fit well training examples with random lables (non-meaningful patterns):
Are deep models robust to massive noise intrinsically? Permalink
Intuitive concepts to keep in mind Permalink
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.