gptkbp:instance_of
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gptkb:neural_networks
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gptkbp:applies_to
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image classification
object detection
semantic segmentation
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gptkbp:based_on
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gptkb:Transformers
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gptkbp:competes_with
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gptkb:Vision_Transformers
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gptkbp:developed_by
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gptkb:Microsoft_Research
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gptkbp:has
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attention mechanism
layer normalization
hierarchical feature maps
multi-scale feature representation
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gptkbp:has_achieved
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state-of-the-art performance
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https://www.w3.org/2000/01/rdf-schema#label
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Swin-T
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gptkbp:improves
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computational efficiency
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gptkbp:introduced_in
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gptkb:2021
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gptkbp:is_adopted_by
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research institutions
startups
tech companies
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gptkbp:is_compatible_with
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gptkb:NVIDIA_GPUs
gptkb:TPUs
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gptkbp:is_documented_in
|
research papers
technical reports
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gptkbp:is_enhanced_by
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data augmentation techniques
regularization methods
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gptkbp:is_evaluated_by
|
gptkb:Open_Images_dataset
gptkb:PASCAL_VOC_dataset
gptkb:COCO_dataset
gptkb:Cityscapes_dataset
gptkb:ADE20_K_dataset
gptkb:LSUN_dataset
Kaggle competitions
Image Net-21 K dataset
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gptkbp:is_implemented_in
|
gptkb:Tensor_Flow
gptkb:Py_Torch
|
gptkbp:is_influenced_by
|
gptkb:neural_networks
gptkb:Vision_Transformers
|
gptkbp:is_known_for
|
high accuracy
modular design
scalability
robustness to noise
layer-wise training
flexibility in various tasks
|
gptkbp:is_optimized_for
|
GPU acceleration
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gptkbp:is_part_of
|
deep learning frameworks
AI research community
computer vision benchmarks
AI model zoo
image processing pipeline
Swin Transformer family
|
gptkbp:is_popular_in
|
gptkb:academic_research
industry applications
|
gptkbp:is_supported_by
|
tutorials
community contributions
open-source libraries
|
gptkbp:is_trained_in
|
gptkb:Image_Net_dataset
large-scale datasets
|
gptkbp:is_used_by
|
gptkb:developers
gptkb:researchers
|
gptkbp:is_used_in
|
semi-supervised learning
self-supervised learning
|
gptkbp:supports
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transfer learning
|
gptkbp:used_for
|
computer vision tasks
|
gptkbp:utilizes
|
shifted windows
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gptkbp:bfsParent
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gptkb:Swin_Transformer
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gptkbp:bfsLayer
|
5
|