Overview
- 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):
- Derivative Manipulation and IMAE
- Progressive Self Label Correction (ProSelfLC) for Training Robust Deep Neural Networks
- Understanding deep learning requires rethinking generalization
- A Closer Look at Memorization in Deep Networks
- Fortunately, the concept of adversarial examples become universe/unrestricted now, i.e., any examples that fool a model can be viewed as a adversary. For example:
- Examples with noisy labels which are fitted well during training;
- Out-of-distribution data points which are fitted well during training or get high confidence scores during testing;
- Examples with small pixel perturbation and perceptually ignorable which fool a model.
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? Permalink
- No: DNNs can fit well training examples with random lables.
- You may have your own answer if you read Featured Research Delivering, ProSelfLC & Confidence penalty & Label Smoothing & Ouput Regularisation
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.
