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Found 6 papers, 3 papers with code
Online Continual Learning Without the Storage Constraint
In this paper, we target such applications, investigating the online continual learning problem under relaxed storage constraints and limited computational budgets.
Computationally Budgeted Continual Learning: What Does Matter?
Our conclusions are consistent in a different number of stream time steps, e. g., 20 to 200, and under several computational budgets.
A Continuous Mapping For Augmentation Design
Automated data augmentation (ADA) techniques have played an important role in boosting the performance of deep models.
Multilevel Knowledge Transfer for Cross-Domain Object Detection
The key ingredients to our approach are -- (a) mapping the source to the target domain on pixel-level; (b) training a teacher network on the mapped source and the unannotated target domain using adversarial feature alignment; and (c) finally training a student network using the pseudo-labels obtained from the teacher.
Simulation-Based Inference for Global Health Decisions
The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision makers about suitable prevention and containment strategies.
The Intriguing Effects of Focal Loss on the Calibration of Deep Neural Networks
When combined with temperature scaling, focal loss, whilst preserving accuracy and yielding state-of-the-art calibrated models, also preserves the confidence of the model's correct predictions, which is extremely desirable for downstream tasks.
