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Found 6 papers, 1 papers with code
GRASP: A Rehearsal Policy for Efficient Online Continual Learning
We evaluate GRASP and other policies by conducting CL experiments on the large-scale ImageNet-1K and Places-LT image classification datasets.
Overcoming the Stability Gap in Continual Learning
Addressing this problem would enable learning new data with fewer network updates, resulting in increased computational efficiency.
How Efficient Are Today's Continual Learning Algorithms?
There is more to continual learning than mitigating catastrophic forgetting.
SIESTA: Efficient Online Continual Learning with Sleep
Compared to REMIND and prior arts, SIESTA is far more computationally efficient, enabling continual learning on ImageNet-1K in under 2 hours on a single GPU; moreover, in the augmentation-free setting it matches the performance of the offline learner, a milestone critical to driving adoption of continual learning in real-world applications.
Image Segmentation of Zona-Ablated Human Blastocysts
Automating human preimplantation embryo grading offers the potential for higher success rates with in vitro fertilization (IVF) by providing new quantitative and objective measures of embryo quality.
Inner Cell Mass and Trophectoderm Segmentation in Human Blastocyst Images using Deep Neural Network
Embryo quality assessment based on morphological attributes is important for achieving higher pregnancy rates from in vitro fertilization (IVF).
