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Found 4 papers, 1 papers with code
Multimodal Prototypical Networks for Few-shot Learning
Although providing exceptional results for many computer vision tasks, state-of-the-art deep learning algorithms catastrophically struggle in low data scenarios.
Learning to remember: Dynamic Generative Memory for Continual Learning
Continuously trainable models should be able to learn from a stream of data over an undefined period of time.
Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning
In order to tackle these challenges, we introduce Dynamic Generative Memory (DGM) - a synaptic plasticity driven framework for continual learning.
Ranked #4 on
Continual Learning
on ImageNet-50 (5 tasks)
Low-Shot Learning from Imaginary 3D Model
Since the advent of deep learning, neural networks have demonstrated remarkable results in many visual recognition tasks, constantly pushing the limits.
