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Found 9 papers, 4 papers with code
Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization
Given the growing trend of continual learning techniques for deep neural networks focusing on the domain of computer vision, there is a need to identify which of these generalizes well to other tasks such as human activity recognition (HAR).
Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis
This paper aims to assess to what extent such continual learning techniques can be applied to the HAR domain.
A simple normalization technique using window statistics to improve the out-of-distribution generalization on medical images
In this study, we present a novel normalization technique called window normalization (WIN) to improve the model generalization on heterogeneous medical images, which is a simple yet effective alternative to existing normalization methods.
Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images
Specifically, the encoder of a DL model that is pre-trained on the source domain is used to initialize the encoder of a reconstruction model.
CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound
Precise and rapid categorization of images in the B-scan ultrasound modality is vital for diagnosing ocular diseases.
An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices
In recent years we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality.
Towards Automated Animal Density Estimation with Acoustic Spatial Capture-Recapture
They can process large volumes of data quickly, but they do not detect all vocalisations and they do generate some false positives (vocalisations that are not from the target species).
Hadamard Domain Training with Integers for Class Incremental Quantized Learning
In this paper, we propose a technique that leverages inexpensive Hadamard transforms to enable low-precision training with only integer matrix multiplications.
TTMFN: Two-stream Transformer-based Multimodal Fusion Network for Survival Prediction
In TTMFN, we present a two-stream multimodal co-attention transformer module to take full advantage of the complex relationships between different modalities and the potential connections within the modalities.
