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Found 21 papers, 3 papers with code
Pseudo-label Learning with Calibrated Confidence Using an Energy-based Model
In pseudo-labeling (PL), which is a type of semi-supervised learning, pseudo-labels are assigned based on the confidence scores provided by the classifier; therefore, accurate confidence is important for successful PL.
Multi-Scale Spatio-Temporal Graph Convolutional Network for Facial Expression Spotting
In this paper, we propose a Multi-Scale Spatio-Temporal Graph Convolutional Network (SpoT-GCN) for facial expression spotting.
Analyzing Font Style Usage and Contextual Factors in Real Images
There are various font styles in the world.
A Hybrid of Generative and Discriminative Models Based on the Gaussian-coupled Softmax Layer
In this paper, we propose a method to train a hybrid of discriminative and generative models in a single neural network (NN), which exhibits the characteristics of both models.
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data
This paper proposes a deep Bayesian active-learning-to-rank, which trains a Bayesian convolutional neural network while automatically selecting appropriate pairs for relative annotation.
Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels
Ulcerative colitis (UC) classification, which is an important task for endoscopic diagnosis, involves two main difficulties.
Meta-learning of Pooling Layers for Character Recognition
As part of our framework, a parameterized pooling layer is proposed in which the kernel shape and pooling operation are trainable using two parameters, thereby allowing flexible pooling of the input data.
Layer-Wise Interpretation of Deep Neural Networks Using Identity Initialization
The interpretability of neural networks (NNs) is a challenging but essential topic for transparency in the decision-making process using machine learning.
Handwriting Prediction Considering Inter-Class Bifurcation Structures
In this paper, we propose a temporal prediction model that can deal with this bifurcation structure.
Regularized Pooling
This means that max pooling is too flexible to compensate for actual deformations.
A Neural Network Based on the Johnson $S_\mathrm{U}$ Translation System and Related Application to Electromyogram Classification
In this study, a discriminative model based on the multivariate Johnson $S_\mathrm{U}$ translation system is transformed into a linear combination of coefficients and input vectors using log-linearization.
A Discriminative Gaussian Mixture Model with Sparsity
We propose a sparse classifier based on a discriminative GMM, referred to as a sparse discriminative Gaussian mixture (SDGM).
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis
This paper proposes a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns.
SDGM: Sparse Bayesian Classifier Based on a Discriminative Gaussian Mixture Model
In the SDGM, a GMM-based discriminative model is trained by sparse Bayesian learning.
Modality Conversion of Handwritten Patterns by Cross Variational Autoencoders
This research attempts to construct a network that can convert online and offline handwritten characters to each other.
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection
In this context, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the data lack in the real image distribution; researchers have improved classification by augmenting data with noise-to-image (e. g., random noise samples to diverse pathological images) or image-to-image GANs (e. g., a benign image to a malignant one).
A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction
In this paper, we propose a trainable multiplication layer (TML) for a neural network that can be used to calculate the multiplication between the input features.
GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks
In GlyphGAN, the input vector for the generator network consists of two vectors: character class vector and style vector.
ProbAct: A Probabilistic Activation Function for Deep Neural Networks
We show that ProbAct increases the classification accuracy by +2-3% compared to ReLU or other conventional activation functions on both original datasets and when datasets are reduced to 50% and 25% of the original size.
Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks
GAN-based generative models only learn the projection between a random distribution as input data and the distribution of training data. Therefore, the relationship between input and generated data is unclear, and the characteristics of the data generated from this model cannot be controlled.
Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection
Due to the lack of available annotated medical images, accurate computer-assisted diagnosis requires intensive Data Augmentation (DA) techniques, such as geometric/intensity transformations of original images; however, those transformed images intrinsically have a similar distribution to the original ones, leading to limited performance improvement.
