SqueezeNet

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SqueezeNet topic overview

The analysis highlights History, Works and Products as prominent areas in the source structure around SqueezeNet.

Related topics: 21
Source areas: 4
Connected nodes: 25
Extracted relationships: 57
Concept neighborhoods: 11
Bridge connections: 25

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Version history · 12 topics

Overview · 7 topics

Relationship to other networks · 1 topics

Variants · 1 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

License: BSD license
Original authors: Forrest Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, Bill Dally, Kurt Keutzer
Release: 22 February 2016; 10 years ago (2016-02-22)
Repository: github.com/DeepScale/SqueezeNet
Stable release: v1.1 (June 6, 2016; 10 years ago (2016-06-06))
Type: Deep neural network

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

Version history

Relationship to other networks

Model compression

Variants

Style transfer Neural Style Transfer

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How SqueezeNet connects Entity context

The extracted context around SqueezeNet shows recurring relationship patterns in the source. For example, SqueezeNet → Apache MXNet, Apple CoreML, As, Baidu, Below, Caffe, Chainer, Eddie Bell, February, FPGAs, Guo Haria, Imagination Technologies, In, Keras, On February, On June, On March, PyTorch, Shortly, Synopsys Another extracted example is SqueezeNet → AlexNet, AlexNet-level, DNN, However, ImageNet, MB, Rather, This, What SqueezeNet. Use these groups to spot repeated connection types before inspecting the individual relationships.

Top relations

related to history · 24

SqueezeNet → Apache MXNet, Apple CoreML, As, Baidu, Below, Caffe, Chainer, Eddie Bell, February, FPGAs, Guo Haria, Imagination Technologies, In, Keras, On February, On June, On March, PyTorch, Shortly, Synopsys

related to AlexNet · 9

SqueezeNet → AlexNet, AlexNet-level, DNN, However, ImageNet, MB, Rather, This, What SqueezeNet

related to Model compression · 8

SqueezeNet → AlexNet, Deep Compression, DNNs, In, KB, MB, Model, VGG

related to Variants · 3

SqueezeNet → As, In, Some

is a · 2

SqueezeNet → deep neural network for image classification released in 2016, entirely different DNN architecture than AlexNet

License · 1

SqueezeNet → BSD license

Original authors · 1

SqueezeNet → Forrest Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, Bill Dally, Kurt Keutzer

Release · 1

SqueezeNet → 22 February 2016; 10 years ago (2016-02-22)

Repository · 1

SqueezeNet → github.com/DeepScale/SqueezeNet

Stable release · 1

SqueezeNet → v1.1 (June 6, 2016; 10 years ago (2016-06-06))

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

deep model neural network alexnet released 2016 accuracy learning frameworks parameters size classification compression original framework deepscale version imagenet open-source

SqueezeNet relationships Subject–Predicate–Object triples

TTTA extracted 57 structured relationships around SqueezeNet. Examples in this analysis include SqueezeNet → License → BSD license and SqueezeNet → Original authors → Forrest Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, Bill Dally, Kurt Keutzer. The table shows each extracted connection, where it came from and its confidence.

Subject	Predicate	Object	Confidence	Src
SqueezeNet	License	BSD license	1.00	infobox
SqueezeNet	Original authors	Forrest Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, Bill Dally, Kurt Keutzer	1.00	infobox
SqueezeNet	Release	22 February 2016; 10 years ago (2016-02-22)	1.00	infobox
SqueezeNet	Repository	github.com/DeepScale/SqueezeNet	1.00	infobox
SqueezeNet	Stable release	v1.1 (June 6, 2016; 10 years ago (2016-06-06))	1.00	infobox
SqueezeNet	Type	Deep neural network	1.00	infobox
SqueezeNet	is a	deep neural network for image classification released in 2016	0.90	text
SqueezeNet	is a	entirely different DNN architecture than AlexNet	0.90	text
smartphones	instance of	and Synopsys demonstrated SqueezeNet running on low-power processing platforms	0.80	text
FPGAs	instance of	and Synopsys demonstrated SqueezeNet running on low-power processing platforms	0.80	text
and custom processors.As of 2018	instance of	and Synopsys demonstrated SqueezeNet running on low-power processing platforms	0.80	text
SqueezeNet ships	instance of	and Synopsys demonstrated SqueezeNet running on low-power processing platforms	0.80	text

Related concept clusters Concept neighborhoods

The concept neighborhoods around SqueezeNet bring nearby vocabulary together. In this analysis, examples include Frameworks, Learning and Framework. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

deep neural network
- Network
- Neural
- Learning
- Squeezenet
- Parameters
- Applied
- Compression
- Original
- Frameworks
- Released
- Model
- February
model compression
- Applied
- Size
- Also
- Authors
- Compression
- Model
- Deep
- Original
- Parameters
- Demonstrated
- Forrest
- Iandola
SqueezeNet
- Frameworks
- Learning
- Framework
- Original
- Community
- Open-source
- Port
- Research
- Version
- Apache
- Authors
- Demonstrated
squeezenet
- Frameworks
- Learning
- Framework
- Original
- Community
- Open-source
- Port
- Research
- Version
- Apache
- Authors
- Demonstrated
alexnet
- Also
- Imagenet
- Classification
- Version
- Compression
- Model
- Squeezenet
- Authors
- Forrest
- Iandola
- Image
- Networks
version history
- Original
- Alexnet
- Also
- Authors
- Forrest
- Iandola
- Networks
- Compression
- Framework
- Learning
- Squeezenet
- Model
image classification
- Classification
- Image
- Imagenet
- Accuracy
- Alexnet
- Released
- Size
- Network
- Neural
- Model
- Squeezenet
- Deep
apache mxnet
- Mxnet
- Number
- Port
- Framework
- Frameworks
- Learning
- Released
- Squeezenet
- Deep

Connections between topic areas Semantic bridges

For SqueezeNet, one of the stronger structural bridges in this analysis connects SqueezeNet with Version history. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3

SqueezeNet — Version history · splits 13 ⟂ 13

SqueezeNet — Overview · splits 18 ⟂ 8

Map overview Semantic statistics

SqueezeNet

Nodes26

Edges25

Triples57

Avg. degree1.92

Density0.076923

Components1

Source & methodology

TTTA analyzes the structure around SqueezeNet to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Works & Products, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — SqueezeNet · EN edition · Analysis: TopicsToTalkAbout