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Found 16 papers, 6 papers with code
PAMI: partition input and aggregate outputs for model interpretation
There is an increasing demand for interpretation of model predictions especially in high-risk applications.
Adaptively Integrated Knowledge Distillation and Prediction Uncertainty for Continual Learning
The first one is to adaptively integrate multiple levels of old knowledge and transfer it to each block level in the new model.
Assessing and Analyzing the Resilience of Graph Neural Networks Against Hardware Faults
Inspired by the inherent algorithmic resilience of DL methods, this paper conducts, for the first time, a large-scale and empirical study of GNN resilience, aiming to understand the relationship between hardware faults and GNN accuracy.
Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification
Data imbalance between common and rare diseases during model training often causes intelligent diagnosis systems to have biased predictions towards common diseases.
PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification
For the class-center involved triplet loss, the positive and negative samples in each triplet are replaced by their corresponding class centers, which enforces data representations of the same class closer to the class center.
Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography
Compared to supervised learning with labelled disease EEG data which can train a model to analyze specific diseases but would fail to monitor previously unseen statuses, anomaly detection based on only normal EEGs can detect any potential anomaly in new EEGs.
Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor
In this case, updating the intelligent system with data of new diseases would inevitably downgrade its performance on previously learned diseases.
EnHDC: Ensemble Learning for Brain-Inspired Hyperdimensional Computing
Ensemble learning is a classical learning method utilizing a group of weak learners to form a strong learner, which aims to increase the accuracy of the model.
SATS: Self-Attention Transfer for Continual Semantic Segmentation
Considering that pixels belonging to the same class in each image often share similar visual properties, a class-specific region pooling is applied to provide more efficient relationship information for knowledge transfer.
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Automated Architecture Search for Brain-inspired Hyperdimensional Computing
On the Clintox dataset, which tries to learn features from developed drugs that passed/failed clinical trials for toxicity reasons, the searched HDC architecture obtains the state-of-the-art ROC-AUC scores, which are 0. 80% higher than the manually designed HDC and 9. 75% higher than conventional neural networks.
Understanding of Kernels in CNN Models by Suppressing Irrelevant Visual Features in Images
In this paper, a simple yet effective optimization method is proposed to interpret the activation of any kernel of interest in CNN models.
Discriminative Distillation to Reduce Class Confusion in Continual Learning
Successful continual learning of new knowledge would enable intelligent systems to recognize more and more classes of objects.
Preserving Earlier Knowledge in Continual Learning with the Help of All Previous Feature Extractors
In order to reduce the forgetting of particularly earlier learned old knowledge and improve the overall continual learning performance, we propose a simple yet effective fusion mechanism by including all the previously learned feature extractors into the intelligent model.
Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning
Despite tremendous efforts, it is very challenging to generate a robust model to assist in the accurate quantification assessment of COVID-19 on chest CT images.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images
In this paper, we present a study using deep fully convolutional network and ensemble models to automatically detect such WMH using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) scans.
