output
20162026
most citedGAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

353 citations

Showing 2021Show all

5 papers · 1 filter

cs.LG2021★ 27 cited

Differentiable Network Pruning for Microcontrollers

Edgar Liberis, Nicholas D. Lane

Embedded and personal IoT devices are powered by microcontroller units (MCUs), whose extreme resource scarcity is a major obstacle for applications relying on on-device deep learni…

cs.LG2021★ 1 cited

Structured Convolutional Kernel Networks for Airline Crew Scheduling

Yassine Yaakoubi, François Soumis, Simon Lacoste-Julien

Motivated by the needs from an airline crew scheduling application, we introduce structured convolutional kernel networks (Struct-CKN), which combine CKNs from Mairal et al. (2014)…

cs.DC2021★ 37 cited

DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device

Mario Almeida, Stefanos Laskaridis, Stylianos I. Venieris +2

Recently, there has been an explosive growth of mobile and embedded applications using convolutional neural networks(CNNs). To alleviate their excessive computational demands, deve…

cs.CV2021

Improving memory banks for unsupervised learning with large mini-batch, consistency and hard negative mining

Adrian Bulat, Enrique Sánchez-Lozano, Georgios Tzimiropoulos

An important component of unsupervised learning by instance-based discrimination is a memory bank for storing a feature representation for each training sample in the dataset. In t…

cs.LG2021

Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition

Xueting Zhang, Debin Meng, Henry Gouk +1

Current state-of-the-art few-shot learners focus on developing effective training procedures for feature representations, before using simple, e.g. nearest centroid, classifiers. I…