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Found 25 papers, 6 papers with code
Hybrid of DiffStride and Spectral Pooling in Convolutional Neural Networks
Diffstride and Spectral Pooling techniques are expected to maintain most of the information contained in the image.
Learning-Augmented K-Means Clustering Using Dimensional Reduction
Learning augmented is a machine learning concept built to improve the performance of a method or model, such as enhancing its ability to predict and generalize data or features, or testing the reliability of the method by introducing noise and other factors.
MAMI: Multi-Attentional Mutual-Information for Long Sequence Neuron Captioning
Neuron labeling is an approach to visualize the behaviour and respond of a certain neuron to a certain pattern that activates the neuron.
SparseSwin: Swin Transformer with Sparse Transformer Block
Advancements in computer vision research have put transformer architecture as the state of the art in computer vision tasks.
Ranked #1 on
Image Classification
on ImageNet-100
IndoHerb: Indonesia Medicinal Plants Recognition using Transfer Learning and Deep Learning
The CNN transfer learning models used are ResNet34, DenseNet121, and VGG11_bn.
Mixture of Self-Supervised Learning
Therefore, we propose the Gated Self-Supervised Learning method to improve image classification which use more than one transformation as pretext task and uses the Mixture of Expert architecture as a gating network in combining each pretext task so that the model automatically can study and focus more on the most useful augmentations for classification.
Sparse then Prune: Toward Efficient Vision Transformers
In this research, we investigate the possibility of applying Sparse Regularization to Vision Transformers and the impact of Pruning, either after Sparse Regularization or without it, on the trade-off between performance and efficiency.
Synthesis of Batik Motifs using a Diffusion -- Generative Adversarial Network
This research focuses on the use of StyleGAN2-Ada and Diffusion techniques to produce realistic and high-quality synthetic batik patterns.
Text recognition on images using pre-trained CNN
In this research, the recognition is trained by using Chars74K dataset and the best model results then tested on some samples of IIIT-5K-Dataset.
Prediction of COVID-19 by Its Variants using Multivariate Data-driven Deep Learning Models
The Coronavirus Disease 2019 or the COVID-19 pandemic has swept almost all parts of the world since the first case was found in Wuhan, China, in December 2019.
Gated Self-supervised Learning For Improving Supervised Learning
In past research on self-supervised learning for image classification, the use of rotation as an augmentation has been common.
Deep Transformer Model with Pre-Layer Normalization for COVID-19 Growth Prediction
The first confirmed case caused by this virus was found at the end of December 2019 in Wuhan City, China.
Classification of Alzheimer's Disease Using the Convolutional Neural Network (CNN) with Transfer Learning and Weighted Loss
With this technology, image recognition in the early diagnosis of Alzheimer's disease can be achieved automatically using machine learning.
Image Colorization using U-Net with Skip Connections and Fusion Layer on Landscape Images
We present a novel technique to automatically colorize grayscale images that combine the U-Net model and Fusion Layer features.
Mask Usage Recognition using Vision Transformer with Transfer Learning and Data Augmentation
The use of masks is essential in preventing the spread of COVID-19 by identifying an image of a person using a mask.
Attention-effective multiple instance learning on weakly stem cell colony segmentation
It is designed in a single model to produce weak segmentation and classification of colonies without using finely labeled samples.
Large-Scale News Classification using BERT Language Model: Spark NLP Approach
This research aims to study the effect of big data processing on NLP tasks based on a deep learning approach.
Indonesia's Fake News Detection using Transformer Network
Indonesia, as a country with the 4th largest population, has a problem in dealing with fake news.
BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews
Besides the multilingual pre-trained model, we use the pre-trained model that only has been trained in Indonesian.
Weakly-Supervised Action Localization and Action Recognition using Global-Local Attention of 3D CNN
The proposed approach intends to show the usefulness of every layer termed as global-local attention in 3D CNN via visual attribution, weakly-supervised action localization, and action recognition.
U-Net with Graph Based Smoothing Regularizer for Small Vessel Segmentation on Fundus Image
The detection of retinal blood vessels, especially the changes of small vessel condition is the most important indicator to identify the vascular network of the human body.
COVID-19 growth prediction using multivariate long short term memory
To make use of big data in the prediction using deep learning, we use long short-term memory (LSTM) method to learn the correlation of COVID-19 growth over time.
Correlation Net: Spatiotemporal multimodal deep learning for action recognition
The existing fusion approach averages the two streams.
Evaluation of Feature Detector-Descriptor for Real Object Matching under Various Conditions of Ilumination and Affine Transformation
This study attempts to provide explanations, descriptions and evaluations of some most popular and current combinations of description and descriptor frameworks, namely SIFT, SURF, MSER, and BRISK for keypoint extractors and SIFT, SURF, BRISK, and FREAK for descriptors.
Web-Based Implementation of Travelling Salesperson Problem Using Genetic Algorithm
Based on the result, the use of Ruby in GA implementation is recommended.
