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Found 6 papers, 2 papers with code
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with Uncertainty
Knowledge distillation is an effective paradigm for boosting the performance of pocket-size model, especially when multiple teacher models are available, the student would break the upper limit again.
Dive into the Resolution Augmentations and Metrics in Low Resolution Face Recognition: A Plain yet Effective New Baseline
Although deep learning has significantly improved Face Recognition (FR), dramatic performance deterioration may occur when processing Low Resolution (LR) faces.
The Future will be Different than Today: Model Evaluation Considerations when Developing Translational Clinical Biomarker
Finding translational biomarkers stands center stage of the future of personalized medicine in healthcare.
Augmented Geometric Distillation for Data-Free Incremental Person ReID
On this basis, we reveal a "noisy distillation" problem stemming from the noise in dreaming memory, and further propose to augment distillation in a pairwise and cross-wise pattern over different views of memory to mitigate it.
Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning
State-of-the-art methods fall short in the speculative reasoning ability, as they assume the correctness of a fact is solely determined by its presence in KG, making them vulnerable to false negative/positive issues.
Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study
Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies, evoking inefficiencies in sequence modeling.
