Search Results for author:
Found 11 papers, 5 papers with code
Hater-O-Genius Aggression Classification using Capsule Networks
In this paper, we propose an end-to-end ensemble-based architecture to automatically identify and classify aggressive tweets.
Deep Model Compression based on the Training History
First, we train the model and select the filter pairs with redundant filters in each pair.
SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!
The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes.
SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!
The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes.
AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer Learning
The experimental results obtained in this study depict that tuning of the pre-trained CNN layers with the knowledge from the target dataset confesses better transfer learning ability.
An Information-rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos
Traditionally in deep learning based human activity recognition approaches, either a few random frames or every $k^{th}$ frame of the video is considered for training the 3D CNN, where $k$ is a small positive integer, like 4, 5, or 6.
AutoFCL: Automatically Tuning Fully Connected Layers for Handling Small Dataset
Fine-tuning the newly learned (target-dependent) FC layers leads to state-of-the-art performance, according to the experiments carried out in this research.
Online Signature Verification Based on Writer Specific Feature Selection and Fuzzy Similarity Measure
Online Signature Verification (OSV) is a widely used biometric attribute for user behavioral characteristic verification in digital forensics.
OSVNet: Convolutional Siamese Network for Writer Independent Online Signature Verification
Online signature verification (OSV) is one of the most challenging tasks in writer identification and digital forensics.
Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification
To automate the process of learning a CNN architecture, this paper attempts at finding the relationship between Fully Connected (FC) layers with some of the characteristics of the datasets.
RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification
The results of the proposed RCCNet model are compared with five state-of-the-art CNN models in terms of the accuracy, weighted average F1 score and training time.
