CNNs improve medical image classification with few samples.
problem Classifying medical images with limited training data.
method Transfer learning using CNNs, representation extraction, and a novel metric for performance prediction.
result CNN-based transfer learning outperforms feature-based methods with high correlation to test set performance.
In this paper, we address the issue of how to enhance the generalization performance of convolutional neural networks (CNN) in the early learning stage for image classification. This is motivated by real-time applications that require the generalization performance of CNN to be satisfactory within limited training time…
Study improves CNNs for audio scene classification by restricting receptive fields and adding frequency awareness.
problem Improving CNNs for robust acoustic scene classification.
method Investigated different receptive field configurations for various CNN architectures and introduced Frequency Aware CNNs.
result Several well-performing submissions to DCASE 2019 Challenge were achieved.
Efficient CNN for VQA achieves similar performance to standard models.
problem Computational intensity of standard VQA models.
method Proposes a sparsely activated CNN architecture.
result Sparsely activated CNN achieves comparable performance.
New approximative kernels improve PDE-G-CNNs for geometric deep learning.
problem Inaccurate approximations of exact kernels in PDE-G-CNNs.
method Developed new approximative kernels that work regardless of spatial anisotropy.
result New kernels provide better error estimates and maintain reflectional symmetries.
Proposes a fixed smooth convolutional layer to reduce checkerboard artifacts in CNNs.
problem Checkerboard artifacts in CNNs during upsampling and strided convolution.
method Fixed convolutional layer with adjustable smoothness, applied to four CNNs and GANs.
result Significantly improves classification performance and image generation quality.
The paper classifies soil texture using 1D CNNs on hyperspectral data.
problem Classifying soil texture from hyperspectral data.
method Developed and implemented three 1D CNNs: LucasCNN, LucasResNet, and LucasCoordConv.
result LucasCoordConv achieves the best performance in accuracy.
CNNs improve transient detection in DES-SN images.
problem Automated identification of transients in astronomical images.
method Convolutional Neural Networks (CNNs) for image classification.
result CNNs achieve efficiency in identifying non-artifacts from artifacts.
This paper introduces a way to learn cross-modal convolutional neural network (X-CNN) architectures from a base convolutional network (CNN) and the training data to reduce the design cost and enable applying cross-modal networks in sparse data environments. Two approaches for building X-CNNs are presented. The base app…
Optimizes CNN architectures by analyzing receptive fields without training.
problem CNNs often have too many layers, making them resource-intensive and less effective.
method Layer-wise analysis of receptive fields to identify unproductive layers.
result Identifies and removes unproductive layers, improving CNN performance and efficiency.
Unified framework explains geometric properties of CNNs.
problem Understanding why encoder-decoder CNNs perform well.
method Unified mathematical framework based on recent neural network theories.
result Encoder-decoder CNNs are related to nonlinear basis representation using combinatorial convolution frames.
New CNN method improves deconvolution microscopy without PSF measurement.
problem Computational expense and blind estimation in conventional deconvolution microscopy.
method Cycle consistent CNN with explicit PSF modeling layers for blind deconvolution.
result Algorithm robustness and efficacy confirmed through experimental results.
Proposes a curriculum-based scheme to smooth CNN feature embeddings.
problem Distortion artifacts in early training stages of CNNs.
method Smoothes feature embedding using Gaussian kernels to control high-frequency information.
result Significant performance improvements on various vision tasks.
New CNN initialization scheme derived from modern architectures.
problem Stability of CNN model parameters initialization.
method Derived new initialization scheme from modern CNN architectures.
result New initialization method outperforms de facto standard schemes.
Computer vision performances have been significantly improved in recent years by Convolutional Neural Networks(CNN). Currently, applications using CNN algorithms are deployed mainly on general purpose hardwares, such as CPUs, GPUs or FPGAs. However, power consumption, speed, accuracy, memory footprint, and die size sho…
Paper revisits graph-CNNs using Laplace-Beltrami spectral filters and polynomials.
problem Improving spectral graph convolutional neural networks (graph-CNNs).
method Developed Laplace-Beltrami CNN (LB-CNN) by replacing graph Laplacian with LB operator and approximating spectral filters using Chebyshev, Laguerre, and Hermite polynomials.
result Classification accuracy of LB-CNN is not dependent on the type of polynomials or operators.
CNN model predicts fluvial floods quickly and accurately.
problem Real-time flood prediction is computationally demanding.
method Deep Convolutional Neural Network (CNN) trained on 2D hydraulic model outputs.
result CNN model outperforms SVR in predicting flood inundation.
Approximate multipliers boost CNN training speed, power, and area at slight accuracy cost.
problem Improving CNN training performance in terms of speed, power, and area.
method Simulation of approximate multipliers' impact on CNN training, hybrid training method combining approximate and exact multipliers.
result Using approximate multipliers for most of training enhances speed, power, and area with minimal accuracy loss.
Boosting CNNs with dynamic feature selection and boosting weights improves accuracy and efficiency.
problem Expensive training of deep CNNs and manual architecture design.
method Dynamic feature selection, importance sampling, and boosting weights integrated into network training.
result Boosted CNN variants consistently outperform conventional CNNs in accuracy and training speed.
This paper analyzes CNNs for malware detection in cloud IaaS.
problem Malware vulnerability in cloud IaaS environments.
method Analysis of Convolutional Neural Networks (CNNs) for online malware detection using process-level performance metrics.
result State-of-the-art DenseNets and ResNets effectively detect malware in online cloud systems.
Large receptive field CNNs improve distant speech recognition.
problem Degrading performance of ASR systems in noisy environments.
method Investigated large receptive field CNN variants including recursive, dilated, and hourglass networks.
result Stacked hourglass networks show significant improvements in distant speech recognition.
Convolutional neural networks (CNNs) are one of the driving forces for the advancement of computer vision. Despite their promising performances on many tasks, CNNs still face major obstacles on the road to achieving ideal machine intelligence. One is that CNNs are complex and hard to interpret. Another is that standard…
Paper learns an explainer to interpret CNN features without annotations.
problem Interpreting complex features in CNNs without labeled data.
method Unsupervised learning of an explainer to decompose and reconstruct feature maps.
result Explainer learns to reconstruct CNN features without losing information.
Convolutional neural networks (CNN) have achieved state of the art performance on both classification and segmentation tasks. Applying CNNs to microscopy images is challenging due to the lack of datasets labeled at the single cell level. We extend the application of CNNs to microscopy image classification and segmentat…
This paper reports the performances of shallow word-level convolutional neural networks (CNN), our earlier work (2015), on the eight datasets with relatively large training data that were used for testing the very deep character-level CNN in Conneau et al. (2016). Our findings are as follows. The shallow word-level CNN…
Study combines CNNs and LSTMs for ECG classification, improving performance with attention mechanisms.
problem Improving ECG classification accuracy for weakly supervised tasks.
method Investigates ConvLSTM networks and various attention mechanisms to capture both morphological and temporal features.
result Attention mechanisms improve classification performance and provide interpretable saliency maps.
This paper examines how padding affects LSTM and CNN performance.
problem The impact of padding on LSTM and CNN performance.
method Used a sentiment analysis task with various padding techniques on the same dataset for both LSTM and CNN.
result Suggests the best padding technique for improved performance.
A simple CNN architecture outperforms complex ones in P300 detection.
problem Efficiently detecting P300 component from EEG signals.
method Used a simple CNN architecture with a single depthwise separable 1D convolutional layer followed by a fully connected Sigmoid neuron.
result A single depthwise separable 1D convolutional layer with four filters achieved competitive performance.
Few-shot image classification is improved by correcting CNNs' texture bias.
problem Few-shot image classification performance is hindered by CNNs' texture bias.
method Corrected CNNs' texture bias using a simpler method than state-of-the-art approaches.
result State-of-the-art performance on miniImageNet task achieved.
Paper attacks CNNs in EEG BCIs with adversarial examples.
problem Vulnerability of CNN classifiers in EEG-based BCIs.
method UFGSM to attack three popular CNN classifiers in BCIs.
result Demonstrates effectiveness of adversarial attacks on CNNs in BCIs.
Inspired by recent advances in deep learning, we propose a novel iterative BP-CNN architecture for channel decoding under correlated noise. This architecture concatenates a trained convolutional neural network (CNN) with a standard belief-propagation (BP) decoder. The standard BP decoder is used to estimate the coded b…
Large CNNs have delivered impressive performance in various computer vision applications. But the storage and computation requirements make it problematic for deploying these models on mobile devices. Recently, tensor decompositions have been used for speeding up CNNs. In this paper, we further develop the tensor decom…
This paper uses CNN-LSTM to predict stock market performance.
problem Predicting stock market performance is challenging due to changing prices and lack of advanced libraries.
method Developed a CNN-LSTM Neural Network model to track stock data patterns and predict future performance.
result The CNN-LSTM model outperformed other models in predicting stock market performance.
Deep CNN detects mind wandering from EEG data.
problem Detecting mind wandering to reorient attention.
method Channel-wise deep convolutional neural network (CNN) model.
result 91.78% accuracy in detecting mind wandering.
Convolutional Neural Networks (CNNs) require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. In this paper we show that the sample complexity of CNNs can be significantly improved by using 3D roto-translation group convolutions (G-Convs) instead of the more conv…
Unified method for CNNs to approximate equivariant maps across various groups.
problem Limited universal approximation theorems for CNNs with specific groups and settings.
method Unified approach to derive universal approximation theorems for equivariant maps by CNNs in diverse settings.
result Ability to handle non-linear equivariant maps between infinite-dimensional spaces for non-compact groups.
Deep convolutional neural networks (CNNs) can be applied to malware binary detection via image classification. The performance, however, is degraded due to the imbalance of malware families (classes). To mitigate this issue, we propose a simple yet effective weighted softmax loss which can be employed as the final laye…
L-CNNs preserve gauge symmetry in neural networks.
problem Applying machine learning to lattice gauge theory while preserving gauge symmetry.
method L-CNNs use gauge equivariance to construct a gauge equivariant convolutional layer and bilinear layer.
result L-CNNs achieve higher accuracy in non-linear regression tasks compared to non-equivariant CNNs.
Generative model attacks CNN on MNIST by subtly replacing input patterns.
problem Adversarial attacks on neural networks.
method Generative model that replaces input patterns with generated ones.
result Demonstrated effectiveness on MNIST dataset.
Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease have been promising, but it has not yet been shown that models trained on x-rays from one hospital or one group of hospitals will work equally well at different hospitals. Before these tools are used for computer-aided diagnosis i…
Improved anti-cancer drug sensitivity prediction using REFINED CNN ensemble learning.
problem Challenges in predicting anti-cancer drug sensitivity for individual cell lines.
method Using REFINED CNN, which represents high-dimensional vectors as compact 2D images with spatial correlations, and building ensembles of these models.
result Ensemble approaches significantly improve drug sensitivity prediction performance compared to single models.
Improved deep CNNs for ASC by optimizing receptive field size.
problem Deep CNNs perform poorly in ASC compared to simpler models.
method Analyzed and adapted the receptive field of ResNet and DenseNet.
result State-of-the-art performance achieved with optimized receptive field.
Deep learning models based on CNNs are predominantly used in image classification tasks. Such approaches, assuming independence of object categories, normally use a CNN as a feature learner and apply a flat classifier on top of it. Object classes in many settings have hierarchical relations, and classifiers exploiting …
New bounds for CNNs show better generalization than previous models.
problem Improving understanding of CNNs' generalization ability.
method Proposed tighter generalization bounds for CNNs by exploiting the sparse and permutation structure of weight matrices and spectral norms of convolution operations.
result Theoretical and experimental results show tighter bounds for CNNs than existing bounds.
Enhanced CNN kernels improve image classification accuracy.
problem Improving classification accuracy of CNNs.
method Local Average Pooling and random image patch representation.
result 89% accuracy on CIFAR-10, matching AlexNet performance.
New protocol benchmarks deep learning methods' data efficiency.
problem Measuring data efficiency of deep learning methods.
method Proposed experimental protocol to benchmark CNNs and HiGSFA.
result HiGSFA outperforms CNNs on smaller datasets.
In this paper we propose the macroblock scaling (MBS) algorithm, which can be applied to various CNN architectures to reduce their model size. MBS adaptively reduces each CNN macroblock depending on its information redundancy measured by our proposed effective flops. Empirical studies conducted with ImageNet and CIFAR-…
Equivariant CNNs improve RL performance in symmetric environments.
problem Learning equivariant representations for RL in symmetric environments.
method Proposed and studied equivariant CNNs for RL.
result Equivariant CNNs enhance RL performance and sample efficiency.