A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Chao Dong, Chen Change Loy, Kaiming He, et al.
This paper introduces SRCNN, a deep CNN that learns an end-to-end mapping for single image super-resolution, outperforming sparse-coding methods.
Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla
SegNet introduces a deep encoder-decoder architecture for semantic segmentation that uses max-pooling indices for efficient non-linear upsampling, achieving competitive accuracy with low memory and inference time.
Shaoqing Ren, Kaiming He, Ross Girshick, et al.
Introduces a Region Proposal Network (RPN) that shares convolutional features with Fast R-CNN, enabling nearly cost-free region proposals and real-time object detection.
J. Feldmann, N. Youngblood, M. Karpov, et al.
An integrated photonic tensor core using soliton microcombs and phase-change memory achieves over 10^12 MAC operations per second for parallel convolutional processing.
Mohammad Sadegh Norouzzadeh, Anh‐Tu Nguyen, Margaret Kosmala, et al.
This paper demonstrates that deep convolutional neural networks can automatically identify, count, and describe behaviors of 48 animal species in camera-trap images, achieving human-level accuracy and saving years of manual labeling effort.
Aldo Pareja, Giacomo Domeniconi, Jie Chen, et al.
EvolveGCN adapts GCN parameters over time using an RNN to handle dynamic graphs without relying on node embeddings.
Yipeng Hu, Marc Modat, Eli Gibson, et al.
This paper introduces a weakly-supervised CNN approach for multimodal image registration that learns voxel-level transformations from anatomical labels, enabling real-time, fully-automated inference without labels.
Zitian Gao, Lynx Chen, Yihao Xiao, et al.
The paper proposes the Universal Reasoning Model (URM), which enhances universal transformers with short convolution and truncated backpropagation, achieving state-of-the-art results on ARC-AGI benchmarks.
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Introduces Correlated Low-Rank Adaptation (CoLoRA), a novel framework that explicitly models correlations among low-rank factors to improve parameter-efficient fine-tuning of convolutional networks.
Kiranyaz, Mustafa Serkan, Onur Avcı, Osama Abdeljaber, et al.
This paper provides the first comprehensive review of 1D CNNs, covering their architecture, applications, and state-of-the-art performance in fields like biomedical data classification and structural health monitoring.
Eduardo Romera, Jose M. Álvarez, Luis M. Bergasa, et al.
ERFNet introduces a novel efficient residual factorized convolutional layer for real-time semantic segmentation, achieving high accuracy at over 83 FPS on Titan X and 7 FPS on Jetson TX1.
Khalid M. Hosny, Akram M. Mortda, Nabil A. Lashin, et al.
A lightweight CNN with only four convolutional layers achieves high accuracy (99.1-100%) for detecting splicing image forgery across multiple datasets.