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Found 7 papers, 4 papers with code
RRR-Net: Reusing, Reducing, and Recycling a Deep Backbone Network
It has become mainstream in computer vision and other machine learning domains to reuse backbone networks pre-trained on large datasets as preprocessors.
Modularity in Deep Learning: A Survey
We review the notion of modularity in deep learning around three axes: data, task, and model, which characterize the life cycle of deep learning.
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification
We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks.
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resources, restricting their success to domains where such resources are available.
LTU Attacker for Membership Inference
In both cases classification accuracy or error rate are used as the metric: Utility is evaluated with the classification accuracy of the Defender model; Privacy is evaluated with the membership prediction error of a so-called "Leave-Two-Unlabeled" LTU Attacker, having access to all of the Defender and Reserved data, except for the membership label of one sample from each.
OmniPrint: A Configurable Printed Character Synthesizer
We introduce OmniPrint, a synthetic data generator of isolated printed characters, geared toward machine learning research.
Effectiveness of Optimization Algorithms in Deep Image Classification
Adam is applied widely to train neural networks.
