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Found 7 papers, 1 papers with code
Enhancing Cell Tracking with a Time-Symmetric Deep Learning Approach
To address this issue, we aimed to develop a new deep-learning based tracking method that relies solely on the assumption that cells can be tracked based on their spatio-temporal neighborhood, without restricting it to consecutive frames.
Saliency Map Based Data Augmentation
Data augmentation is a commonly applied technique with two seemingly related advantages.
Receptive Field Size Optimization with Continuous Time Pooling
The pooling operation is a cornerstone element of convolutional neural networks.
Sorted Pooling in Convolutional Networks for One-shot Learning
We present generalized versions of the commonly used maximum pooling operation: $k$th maximum and sorted pooling operations which selects the $k$th largest response in each pooling region, selecting locally consistent features of the input images.
A Greedy Approach to Max-Sliced Wasserstein GANs
In this paper we will demonstrate that the approximation of the Wasserstein distance by sorting the samples is not always the optimal approach and the greedy assignment of the real and fake samples can result faster convergence and better approximation of the original distribution.
MimosaNet: An Unrobust Neural Network Preventing Model Stealing
Deep Neural Networks are robust to minor perturbations of the learned network parameters and their minor modifications do not change the overall network response significantly.
Domain Partitioning Network
Standard adversarial training involves two agents, namely a generator and a discriminator, playing a mini-max game.
