Inception v1
E472887
Inception architecture version
convolutional neural network architecture
deep learning model architecture
Inception v1 is the original version of Google’s Inception deep convolutional neural network architecture, introduced for efficient and accurate image classification in the 2014 GoogLeNet model.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Inception v1 canonical | 3 |
How this entity was disambiguated
This entity first appeared as the object of triple T4833481 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Inception v1 Context triple: [Inception architecture, hasVariant, Inception v1]
-
A.
Inception
Inception is a 2010 science fiction heist film directed by Christopher Nolan that explores dream manipulation and shared subconscious worlds.
-
B.
Eames in Inception
Eames in Inception is a charismatic and witty forger on Cobb’s team, known for his ability to impersonate others within dreams and for providing both comic relief and tactical ingenuity in the heist.
-
C.
The Matrix
The Matrix is a groundbreaking 1999 science fiction film that blends cyberpunk action with philosophical themes about reality, featuring innovative visual effects like "bullet time" and a dystopian story of humans trapped in a simulated world.
-
D.
the Matrix
The Matrix is a vast, sentient data repository on Gallifrey that stores the knowledge, memories, and consciousness of Time Lords in the Doctor Who universe.
-
E.
Batman Begins
Batman Begins is a 2005 superhero film directed by Christopher Nolan that reboots the Batman franchise by exploring Bruce Wayne’s origin story in a darker, more realistic style.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Inception v1 Target entity description: Inception v1 is the original version of Google’s Inception deep convolutional neural network architecture, introduced for efficient and accurate image classification in the 2014 GoogLeNet model.
-
A.
Inception
Inception is a 2010 science fiction heist film directed by Christopher Nolan that explores dream manipulation and shared subconscious worlds.
-
B.
Eames in Inception
Eames in Inception is a charismatic and witty forger on Cobb’s team, known for his ability to impersonate others within dreams and for providing both comic relief and tactical ingenuity in the heist.
-
C.
The Matrix
The Matrix is a groundbreaking 1999 science fiction film that blends cyberpunk action with philosophical themes about reality, featuring innovative visual effects like "bullet time" and a dystopian story of humans trapped in a simulated world.
-
D.
the Matrix
The Matrix is a vast, sentient data repository on Gallifrey that stores the knowledge, memories, and consciousness of Time Lords in the Doctor Who universe.
-
E.
Batman Begins
Batman Begins is a 2005 superhero film directed by Christopher Nolan that reboots the Batman franchise by exploring Bruce Wayne’s origin story in a darker, more realistic style.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
Inception architecture version
ⓘ
convolutional neural network architecture ⓘ deep learning model architecture ⓘ |
| achievedResultOn | ImageNet classification ⓘ |
| alsoKnownAs |
GoogLeNet Inception architecture
ⓘ
linked to:
GoogLeNet
|
| basedOn | deep convolutional neural networks ⓘ |
| designedFor | image classification ⓘ |
| developedBy |
Google
ⓘ
Google Research ⓘ |
| field |
computer vision
ⓘ
deep learning ⓘ |
| hasComponent |
1x1 convolution branch
ⓘ
3x3 convolution branch ⓘ 5x5 convolution branch ⓘ concatenation of feature maps ⓘ pooling branch ⓘ |
| hasDesignGoal |
computational efficiency
ⓘ
efficient use of parameters ⓘ high accuracy ⓘ reduced computational cost ⓘ |
| hasKeyIdea |
balancing depth and width with computational budget
ⓘ
factorizing large convolutions into smaller ones via modules ⓘ |
| hasProperty |
deep architecture
ⓘ
parameter efficiency ⓘ sparse connections ⓘ |
| hasSuccessor |
Inception v2
ⓘ
Inception v3 ⓘ
linked to:
Inception architecture
|
| implementedIn |
Caffe
ⓘ
PyTorch ⓘ TensorFlow ⓘ |
| influenced |
later Inception versions
ⓘ
many CNN architectures ⓘ |
| introducedInModel | GoogLeNet ⓘ |
| introducedInPaper |
Going Deeper with Convolutions
ⓘ
linked to:
Inception architecture
|
| introducedInYear | 2014 ⓘ |
| optimizedFor |
ImageNet Large Scale Visual Recognition Challenge
ⓘ
linked to:
ImageNet
|
| partOf |
GoogLeNet architecture
ⓘ
linked to:
GoogLeNet
|
| trainingDataset | ImageNet ⓘ |
| usedFor |
feature extraction
ⓘ
image recognition benchmarks ⓘ transfer learning ⓘ |
| usesConcept |
1x1 convolutions
ⓘ
Inception module ⓘ
linked to:
Inception architecture
dimension reduction ⓘ multi-scale feature extraction ⓘ network-in-network ⓘ parallel convolutional paths ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Inception v1 Description of subject: Inception v1 is the original version of Google’s Inception deep convolutional neural network architecture, introduced for efficient and accurate image classification in the 2014 GoogLeNet model.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.