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Found 8 papers, 1 papers with code
Image captioning with weakly-supervised attention penalty
Ideally, attention maps predicted by captioning models should be consistent with intrinsic attentions from visual models for any given visual concept.
Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach
Recently, semi-supervised learning methods based on generative adversarial networks (GANs) have received much attention.
An attention-based multi-resolution model for prostate whole slide imageclassification and localization
The model achieved state-of-the-art performance for prostate cancer grading with an accuracy of 85. 11\% for classifying benign, low-grade (Gleason grade 3+3 or 3+4), and high-grade (Gleason grade 4+3 or higher) slides on an independent test set.
Semi-supervised Learning using Adversarial Training with Good and Bad Samples
In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training.
Ventral-Dorsal Neural Networks: Object Detection via Selective Attention
The coarse functional distinction between these streams is between object recognition -- the "what" of the signal -- and extracting location related information -- the "where" of the signal.
A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning
Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra- observer agreement.
Generalized Activation via Multivariate Projection
Activation functions are essential to introduce nonlinearity into neural networks, with the Rectified Linear Unit (ReLU) often favored for its simplicity and effectiveness.
Finite Time Performance Analysis of MIMO Systems Identification
This paper is concerned with the finite time identification performance of an n dimensional discrete-time Multiple-Input Multiple-Output (MIMO) Linear Time-Invariant system, with p inputs and m outputs.
