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Found 7 papers, 1 papers with code
A Tensor-based Convolutional Neural Network for Small Dataset Classification
Inspired by the ConvNets with structured hidden representations, we propose a Tensor-based Neural Network, TCNN.
Crafting Adversarial Examples for Neural Machine Translation
We first show the current NMT adversarial attacks may be improperly estimated by the commonly used mono-directional translation, and we propose to leverage the round-trip translation technique to build valid metrics for evaluating NMT adversarial attacks.
How to Accelerate Capsule Convolutions in Capsule Networks
How to improve the efficiency of routing procedures in CapsNets has been studied a lot.
Semantically Stealthy Adversarial Attacks against Segmentation Models
One challenge is making semantically meaningful manipulations across datasets and models.
P-CapsNets: a General Form of Convolutional Neural Networks
The experiment shows that P-CapsNets achieve better performance than CapsNets with varied routing procedures by using significantly fewer parameters on MNIST\&CIFAR10.
Generalized Capsule Networks with Trainable Routing Procedure
To overcome this disadvantages of current routing procedures in CapsNet, we embed the routing procedure into the optimization procedure with all other parameters in neural networks, namely, make coupling coefficients in the routing procedure become completely trainable.
Detecting Small, Densely Distributed Objects with Filter-Amplifier Networks and Loss Boosting
Detecting small, densely distributed objects is a significant challenge: small objects often contain less distinctive information compared to larger ones, and finer-grained precision of bounding box boundaries are required.
