Inception v4
E472889
Inception architecture variant
convolutional neural network architecture
deep learning model
image classification model
Inception v4 is an advanced deep convolutional neural network model for image recognition that refines and extends earlier Inception architectures to achieve higher accuracy and efficiency.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Inception v4 canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4833484 — 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 v4 Context triple: [Inception architecture, hasVariant, Inception v4]
-
A.
Inception
Inception is a 2010 science fiction heist film directed by Christopher Nolan that explores dream manipulation and shared subconscious worlds.
-
B.
Elysium
Elysium is a 2013 science fiction film directed by Neill Blomkamp, set in a dystopian future where a wealthy elite live on a luxurious space habitat while the rest of humanity struggles on an overpopulated, ruined Earth.
-
C.
Elysium
Elysium is the blissful afterlife realm in ancient Greek belief where especially virtuous or heroic souls enjoyed eternal happiness.
-
D.
The Matrix Resurrections
The Matrix Resurrections is a 2021 science fiction action film and the fourth installment in the Matrix franchise, revisiting its cyberpunk world with returning and new characters under the direction of Lana Wachowski.
-
E.
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.
- 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 v4 Target entity description: Inception v4 is an advanced deep convolutional neural network model for image recognition that refines and extends earlier Inception architectures to achieve higher accuracy and efficiency.
-
A.
Inception
Inception is a 2010 science fiction heist film directed by Christopher Nolan that explores dream manipulation and shared subconscious worlds.
-
B.
Elysium
Elysium is a 2013 science fiction film directed by Neill Blomkamp, set in a dystopian future where a wealthy elite live on a luxurious space habitat while the rest of humanity struggles on an overpopulated, ruined Earth.
-
C.
Elysium
Elysium is the blissful afterlife realm in ancient Greek belief where especially virtuous or heroic souls enjoyed eternal happiness.
-
D.
The Matrix Resurrections
The Matrix Resurrections is a 2021 science fiction action film and the fourth installment in the Matrix franchise, revisiting its cyberpunk world with returning and new characters under the direction of Lana Wachowski.
-
E.
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.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
Inception architecture variant
ⓘ
convolutional neural network architecture ⓘ deep learning model ⓘ image classification model ⓘ |
| achieves | state-of-the-art accuracy on ImageNet at time of publication ⓘ |
| architectureType | deep convolutional neural network ⓘ |
| basedOn | Inception architecture NERFINISHED ⓘ |
| benchmark | ImageNet NERFINISHED ⓘ |
| category | feedforward neural network ⓘ |
| contains |
multiple Inception-A blocks
ⓘ
multiple Inception-B blocks ⓘ multiple Inception-C blocks ⓘ reduction blocks between Inception stages ⓘ |
| describedIn | Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning NERFINISHED ⓘ |
| designGoal |
computational efficiency
ⓘ
higher accuracy ⓘ |
| developedBy |
Google
NERFINISHED
ⓘ
Google Brain NERFINISHED ⓘ |
| field |
computer vision
ⓘ
deep learning ⓘ |
| follows | Inception v3 NERFINISHED ⓘ |
| implementationAvailableIn |
Keras
NERFINISHED
ⓘ
TensorFlow NERFINISHED ⓘ |
| improvesUpon |
GoogLeNet
NERFINISHED
ⓘ
Inception v2 NERFINISHED ⓘ Inception v3 NERFINISHED ⓘ |
| inputDomain | natural images ⓘ |
| inputType | RGB images ⓘ |
| license | open source implementation available ⓘ |
| networkDepth | very deep ⓘ |
| optimization | batch normalization ⓘ |
| paperAuthorsInclude |
Alex Alemi
NERFINISHED
ⓘ
Christian Szegedy NERFINISHED ⓘ Sergey Ioffe NERFINISHED ⓘ Vincent Vanhoucke NERFINISHED ⓘ |
| relatedTo |
Inception-ResNet-v1
NERFINISHED
ⓘ
Inception-ResNet-v2 NERFINISHED ⓘ |
| task |
image classification
ⓘ
image recognition ⓘ |
| trainingDataset | ImageNet Large Scale Visual Recognition Challenge dataset NERFINISHED ⓘ |
| typicalUseCase |
feature extraction from images
ⓘ
transfer learning for vision tasks ⓘ |
| uses |
Inception modules
ⓘ
factorized convolutions ⓘ grid size reduction blocks ⓘ |
| yearProposed | 2016 ⓘ |
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 v4 Description of subject: Inception v4 is an advanced deep convolutional neural network model for image recognition that refines and extends earlier Inception architectures to achieve higher accuracy and efficiency.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.