Data to 14 Oct 2026

Network In Network

arXiv 1312.4400 ↗First posted 16 Dec 2013cs.NE cs.CV cs.LGcited by 605

Not on the map. Why?

Abstract (arXiv)

We propose a novel deep network structure called "Network In Network" (NIN) to enhance model discriminability for local patches within the receptive field. The conventional convolutional layer uses linear filters followed by a nonlinear activation function to scan the input. Instead, we build micro neural networks with more complex structures to abstract the data within the receptive field. We instantiate the micro neural network with a multilayer perceptron, which is a potent function approximator. The feature maps are obtained by sliding the micro networks over the input in a similar manner as CNN; they are then fed into the next layer. Deep NIN can be implemented by stacking mutiple of the above described structure. With enhanced local modeling via the micro network, we are able to utilize global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected layers. We demonstrated the state-of-the-art classification performances with NIN on CIFAR-10 and CIFAR-100, and reasonable performances on SVHN and MNIST datasets.

Key facts. Cited by 605 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Dec 2013.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2020: 1; 2021: 188; 2022: 127; 2023: 116; 2024: 81; 2025: 54; 2026: 38.

605 papers.

Sort:

By area:

2021MLP-Mixer: An all-MLP Architecture for Visioncited by 1,446
2021Transformer in Transformercited by 633
2021Pruning and Quantization for Deep Neural Network Acceleration: A Surveycited by 263
2022Visual Attention Networkcited by 210
2022Unified Contrastive Learning in Image-Text-Label Spacecited by 194
2023A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAScited by 151
2021Visformer: The Vision-friendly Transformercited by 137
2021CycleMLP: A MLP-like Architecture for Dense Predictioncited by 126
2021Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challengescited by 115
2021A Survey of Modern Deep Learning based Object Detection Modelscited by 98
2021Transformer-based Spatial-Temporal Feature Learning for EEG Decodingcited by 97
2021Medical Visual Question Answering: A Surveycited by 93

38 more

2022GSV-Cities: Toward Appropriate Supervised Visual Place Recognitioncited by 93
2021The Devil is in the Margin: Margin-based Label Smoothing for Network Calibrationcited by 91
2022Benign Overfitting in Two-layer Convolutional Neural Networkscited by 91
2021Do Transformer Modifications Transfer Across Implementations and Applications?cited by 87
2023InceptionNeXt: When Inception Meets ConvNeXtcited by 86
2023Calibration in Deep Learning: A Survey of the State-of-the-Artcited by 75
2021Going Deeper through the Gleason Scoring Scale: An Automatic end-to-end System for Histology Prostate Grading and Cribriform Pattern Detectioncited by 68
2021FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizescited by 66
2022A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classificationcited by 65
2021Pufferfish: Communication-efficient Models At No Extra Costcited by 61
2024State Space Model for New-Generation Network Alternative to Transformers: A Surveycited by 59
2021Deep Federated Learning for Autonomous Drivingcited by 54
2022Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregationcited by 52
2023TrustMark: Universal Watermarking for Arbitrary Resolution Imagescited by 51
2021Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospectscited by 49
2022A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibrationcited by 49
2021Biomedical Question Answering: A Survey of Approaches and Challengescited by 46
2021Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localizationcited by 46
2023UNICORN: A Unified Backdoor Trigger Inversion Frameworkcited by 46
2024nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Modelcited by 45
2021Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directionscited by 43
2021Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentationcited by 41
2021PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extractioncited by 40
2022AutoSNN: Towards Energy-Efficient Spiking Neural Networkscited by 40
2023GeoLayoutLM: Geometric Pre-training for Visual Information Extractioncited by 40
2022Training with More Confidence: Mitigating Injected and Natural Backdoors During Trainingcited by 38
2022Visual Attention Methods in Deep Learning: An In-Depth Surveycited by 37
2022Efficient Knowledge Distillation from Model Checkpointscited by 37
2022TINYCD: A (Not So) Deep Learning Model For Change Detectioncited by 36
2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
2022Affine Medical Image Registration with Coarse-to-Fine Vision Transformercited by 35
2023Benign Overfitting for Two-layer ReLU Convolutional Neural Networkscited by 34
2021INTERN: A New Learning Paradigm Towards General Visioncited by 33
2023Dual Focal Loss for Calibrationcited by 33
2021Transformer-based deep imitation learning for dual-arm robot manipulationcited by 32
2022AdaFocal: Calibration-aware Adaptive Focal Losscited by 32
2021Knowledge Distillation as Semiparametric Inferencecited by 29
2021Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Errorcited by 29

50 most cited of 605 All 605 (JSON)