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Found 14 papers, 4 papers with code
DeepCaps: Going Deeper with Capsule Networks
Capsule Network is a promising concept in deep learning, yet its true potential is not fully realized thus far, providing sub-par performance on several key benchmark datasets with complex data.
TextCaps : Handwritten Character Recognition with Very Small Datasets
Our system is useful in character recognition for localized languages that lack much labeled training data and even in other related more general contexts such as object recognition.
Ranked #4 on
Image Classification
on EMNIST-Letters
TimeCaps: Capturing Time Series Data With Capsule Networks
Capsule networks excel in understanding spatial relationships in 2D data for vision related tasks.
Robust open-set classification for encrypted traffic fingerprinting
First, we show that a well-regularized deep learning model improves the open-set classification and then we propose a novel open-set classification method with three variants that perform consistently over multiple datasets.
A Neural Embeddings Approach for Detecting Mobile Counterfeit Apps
We show that further performance increases can be achieved by combining style embeddings with content embeddings.
BreathRNNet: Breathing Based Authentication on Resource-Constrained IoT Devices using RNNs
Increasing popularity of IoT devices makes a strong case for implementing RNN based inferences for applications such as acoustics based authentication, voice commands, and edge analytics for smart homes.
Characterizing and Detecting Money Laundering Activities on the Bitcoin Network
Bitcoin is by far the most popular crypto-currency solution enabling peer-to-peer payments.
A Review of Computer Vision Methods in Network Security
Network security has become an area of significant importance more than ever as highlighted by the eye-opening numbers of data breaches, attacks on critical infrastructure, and malware/ransomware/cryptojacker attacks that are reported almost every day.
A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play Store
We show that a novel approach of combining content embeddings and style embeddings outperforms the baseline methods for image similarity such as SIFT, SURF, and various image hashing methods.
Privacy-Preserving Spam Filtering using Functional Encryption
Spam classification over encrypted emails enables the classifier to classify spam email without accessing the email, hence protects the privacy of email content.
Non-Contrastive Learning-based Behavioural Biometrics for Smart IoT Devices
Behaviour biometrics are being explored as a viable alternative to overcome the limitations of traditional authentication methods such as passwords and static biometrics.
ExCeL : Combined Extreme and Collective Logit Information for Enhancing Out-of-Distribution Detection
Deep learning models often exhibit overconfidence in predicting out-of-distribution (OOD) data, underscoring the crucial role of OOD detection in ensuring reliability in predictions.
Long-Tail Learning with Rebalanced Contrastive Loss
We further demonstrate that the performance of RCL as a standalone loss also achieves state-of-the-art level accuracy.
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey
Finally, we highlight the limitations of the existing work and propose promising research directions that explore adversarial and OOD inputs within a unified framework.
