“I’m really looking to learn from the community and would appreciate any feedback, suggestions, or recommendations whether it’s about features, design, usability, or areas for improvement.”

“I built this 3D sim to visualize how a 1D-CNN processes time-series data (the yellow box is the kernel sliding across time).”

“EfficientNet-B0 outperformed DenseNet121, achieving an accuracy of 84.6%, F1-score of 0.8899, and MCC of 0.6849.”

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“Transfer learning with fine-tuning substantially outperforms CNNs trained from scratch for pediatric pneumonia detection, showing near-perfect accuracy.”

“"CNN（畳み込みニューラルネットワーク）は理解できたが、RNN（リカレントニューラルネットワーク）がスッと理解できない"”

“Certain compression strategies not only preserve but can also improve robustness, particularly on networks with more complex architectures.”

“The model successfully predicted drag reduction rates ranging from $-1\%$ to $86\%$, with a mean absolute error of 9.2.”

“The paper's key finding is that existing SOTA 3D semantic segmentation models (FPT, PTv3, OA-CNNs) show significant limitations when applied to the created post-disaster dataset.”

“The paper showcases that such methods have great promise for the deconvolution of reverberation mapping data products.”

“The speed of information displacement is linearly related to the ratio of odd vs total kernel energy.”

“The device enables self-powered, polarization-sensitive and frequency-selective THz detection across a broad response spectrum from 0.25 to 4.24 THz, with a responsivity of 6 V/W, a response time of 62 ms, and mechanical robustness maintained over 2000 bending cycles.”

“The diffusion actions selected by deep Q-learning at different iterations indeed composite a stochastic anisotropic diffusion process with strong adaptivity to different image structures, which enjoys improvement over the traditional ones.”

“The hybrid CNN-LSTM achieves the highest accuracy and stability while maintaining low computational cost across the investigated DM range.”

“GCA-ResUNet achieves Dice scores of 86.11% and 92.64% on Synapse and ACDC benchmarks, respectively, outperforming a range of representative CNN and Transformer-based methods.”

“DehazeSNN is highly competitive to state-of-the-art methods on benchmark datasets, delivering high-quality haze-free images with a smaller model size and less multiply-accumulate operations.”

“The paper's core contribution is "DivQAT, a novel algorithm to train quantized CNNs based on Quantization Aware Training (QAT) aiming to enhance their robustness against extraction attacks."”

“The VBSF architecture achieves an accuracy of more than 98%.”

“SC-Net outperforms state-of-the-art methods in relative pose estimation and outlier removal tasks on YFCC100M and SUN3D datasets.”

“FedOLF achieves at least 0.3%, 6.4%, 5.81%, 4.4%, 6.27% and 1.29% higher accuracy than existing works respectively on EMNIST (with CNN), CIFAR-10 (with AlexNet), CIFAR-100 (with ResNet20 and ResNet44), and CINIC-10 (with ResNet20 and ResNet44), along with higher energy efficiency and lower memory footprint.”

“The paper demonstrates that the target density (rho) of parameters can be achieved in FL, under data and client participation heterogeneity, with minimal loss in statistical performance.”

“TYTAN achieves ~2 times performance improvement, with ~56% power reduction and ~35 times lower area compared to the baseline open-source NVIDIA Deep Learning Accelerator (NVDLA) implementation.”

“The Transformer+SNM configuration attains near-theoretical performance, achieving an AUROC of 0.9977 and an unknown gas detection rate of 99.57% (TPR at 5% FPR).”

“The input is a bit unusual - it's not a fixed-size image or sequence. Each sample has 105 nodes with 8 features per node (coordinates, material properties, derived physical quantities), and I need to predict 105 displacement values.”

“"so I have a simple workshop that needs me to create a baseline model using ONLY single layers of Conv2D, MaxPooling2D, Flatten and Dense Layers in order to classify 10 simple digits."”

“"tried out cnns and resnets, for 3d models they underfit significantly. Any suggestions for NN architectures."”

“INTERACT-CMIL achieves consistent improvements over CNN and foundation-model (FM) baselines, with relative macro F1 gains up to 55.1% (WHO4) and 25.0% (vertical spread).”

“CLAdapter achieves state-of-the-art performance across diverse data-limited scientific domains, demonstrating its effectiveness in unleashing the potential of foundation vision models via adaptive transfer.”

“The study highlights the potential of deep learning methods in enhancing the diagnosis of respiratory diseases such as COVID-19, lung cancer, and pneumonia from chest x-rays.”

“Transformer models, which excel at handling long-term dependencies, have become significant architectural components for time series forecasting.”

“Starkindler provides uncertainty estimates that are regularised by aleatoric uncertainty, and is designed to be more interpretable.”

“The method achieves a state-of-the-art score of 68.10% on the ICBHI 2017 dataset, outperforming existing CNN and hybrid baselines. More importantly, it reaches a sensitivity of 68.31%, a crucial improvement for reliable clinical screening.”

“FluenceFormer with Swin UNETR achieves the strongest performance among the evaluated models and improves over existing benchmark CNN and single-stage methods, reducing Energy Error to 4.5% and yielding statistically significant gains in structural fidelity (p < 0.05).”

“DuaDeep-SeqAffinity significantly outperforms individual architectural components and existing state-of-the-art (SOTA) methods.”

“The EfficientNet-B0 + DenseNet121 (Eff+Den) fusion model achieves the best overall mean performance (accuracy: 82.89%) with balanced class-wise F1-scores.”

“The model achieves promising results by mimicking the multi-dimensional stock numbers as a vector of historical data matrices (read images).”

“CellMamba outperforms both CNN-based, Transformer-based, and Mamba-based baselines in accuracy, while significantly reducing model size and inference latency.”

“The multi-path architecture achieves superior performance over baseline CNN and MLP models, with accuracy exceeding 95% and AUC around 97% for Rifampicin (RIF) and Rifabutin (RFB) resistance prediction.”

“The study focuses on a hybrid CNN-ViT model and fixed-threshold evaluation.”

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“The research uses convolutional neural networks to analyze data from the FAST telescope.”

“The study utilizes blood cell images for malaria diagnosis.”

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“The research is sourced from ArXiv.”

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“The research focuses on explainable Transformer-CNN fusion.”

“The research uses an attention-enhanced CNN.”

“A 28nm 0.22 μJ/token memory-compute-intensity-aware CNN-Transformer accelerator is presented.”