# Advanced Neural Network Applications

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/advanced-neural-network-applications/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the application of deep learning, particularly convolutional neural networks, in computer vision tasks such as image recognition, object detection, and semantic segmentation. It covers various neural network architectures, model compression techniques, and their applications in fields like autonomous driving. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t10036 |
| Works | 379 |

## Topic papers all

Showing 15 of 379.

- [ImageNet Large Scale Visual Recognition Challenge](https://scholariq.org/papers/imagenet-large-scale-visual-recognition-challenge/)
- [3D Object Representations for Fine-Grained Categorization](https://scholariq.org/papers/3d-object-representations-for-fine-grained-categorization/)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with\n Transformers](https://scholariq.org/papers/segformer-simple-and-efficient-design-for-semantic-segmentation-with-n/)
- [CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope](https://scholariq.org/papers/cnn-variants-for-computer-vision-history-architecture-application-challenges-and/)
- [Autonomous vehicle perception: The technology of today and tomorrow](https://scholariq.org/papers/autonomous-vehicle-perception-the-technology-of-today-and-tomorrow/)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://scholariq.org/papers/segformer-simple-and-efficient-design-for-semantic-segmentation-with/)
- [Large Selective Kernel Network for Remote Sensing Object Detection](https://scholariq.org/papers/large-selective-kernel-network-for-remote-sensing-object-detection/)
- [An Analysis Of Convolutional Neural Networks For Image Classification](https://scholariq.org/papers/an-analysis-of-convolutional-neural-networks-for-image-classification/)
- [Convolutional Neural Network (CNN) for Image Detection and Recognition](https://scholariq.org/papers/convolutional-neural-network-cnn-for-image-detection-and-recognition/)
- [A Comprehensive Survey of Image Augmentation Techniques for Deep Learning](https://scholariq.org/papers/a-comprehensive-survey-of-image-augmentation-techniques-for-deep-learning/)
- [Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications](https://scholariq.org/papers/performance-analysis-of-google-colaboratory-as-a-tool-for-accelerating-deep/)
- [Understanding adversarial attacks on deep learning based medical image analysis systems](https://scholariq.org/papers/understanding-adversarial-attacks-on-deep-learning-based-medical-image-analysis/)
- [RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment](https://scholariq.org/papers/rgb-infrared-cross-modality-person-re-identification-via-joint-pixel-and-feature/)
- [Self-challenging Improves Cross-Domain Generalization](https://scholariq.org/papers/self-challenging-improves-cross-domain-generalization/)
- [Fine-grained recognition without part annotations](https://scholariq.org/papers/fine-grained-recognition-without-part-annotations/)

## Topic primary papers

Showing 15 of 115.

- [3D Object Representations for Fine-Grained Categorization](https://scholariq.org/papers/3d-object-representations-for-fine-grained-categorization/)
- [CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope](https://scholariq.org/papers/cnn-variants-for-computer-vision-history-architecture-application-challenges-and/)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://scholariq.org/papers/segformer-simple-and-efficient-design-for-semantic-segmentation-with/)
- [An Analysis Of Convolutional Neural Networks For Image Classification](https://scholariq.org/papers/an-analysis-of-convolutional-neural-networks-for-image-classification/)
- [Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications](https://scholariq.org/papers/performance-analysis-of-google-colaboratory-as-a-tool-for-accelerating-deep/)
- [Fine-grained recognition without part annotations](https://scholariq.org/papers/fine-grained-recognition-without-part-annotations/)
- [BlockDrop: Dynamic Inference Paths in Residual Networks](https://scholariq.org/papers/blockdrop-dynamic-inference-paths-in-residual-networks/)
- [Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review](https://scholariq.org/papers/deep-learning-sensor-fusion-for-autonomous-vehicle-perception-and-localization-a/)
- [Scaling for edge inference of deep neural networks](https://scholariq.org/papers/scaling-for-edge-inference-of-deep-neural-networks/)
- [Don't Decay the Learning Rate, Increase the Batch Size](https://scholariq.org/papers/don-t-decay-the-learning-rate-increase-the-batch-size/)
- [Dynamic Adaptive DNN Surgery for Inference Acceleration on the Edge](https://scholariq.org/papers/dynamic-adaptive-dnn-surgery-for-inference-acceleration-on-the-edge/)
- [Research on data augmentation for image classification based on convolution neural networks](https://scholariq.org/papers/research-on-data-augmentation-for-image-classification-based-on-convolution/)
- [Polarized self-attention: Towards high-quality pixel-wise mapping](https://scholariq.org/papers/polarized-self-attention-towards-high-quality-pixel-wise-mapping/)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://scholariq.org/papers/glm-130b-an-open-bilingual-pre-trained-model/)
- [Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review](https://scholariq.org/papers/comparison-of-vision-transformers-and-convolutional-neural-networks-in-medical/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.