Unsupervised High-level Feature Learner
Sin estimación
No hay requisitos de hardware para este modelo
Los pesos de este modelo no han sido publicados, por lo que no se puede descargar ni ejecutar en su propio hardware en ningún tamaño. Solo es accesible a través de su proveedor, y ninguna tarjeta gráfica lo cambia.
En registro
Especificación completa
Todo lo registrado para este modelo. La mayoría de ello describe cómo fue entrenado en lugar de cómo se ejecuta — contexto útil para juzgar cuánto trabajo se dedicó a ello y cómo se compara con modelos construidos a una escala diferente.
Origen
¿Quién construyó este modelo, dónde y cuándo fue publicado?
- Organización
- Tipo de organización
- Industry
- País
- United States of America
- Publicado
- 12 July 2012
- Autores
- Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeff Dean, Andrew Y. Ng
Lo que hace
Las áreas problemáticas para las que se construyó el modelo. Un modelo puede llevar varios de cada uno.
- Dominio
- Vision
- Tarea
- Image classification
- Enfoque
- Unsupervised
Tamaño
Qué tan grande es el modelo y cuántos datos se utilizaron para entrenarlo. Los parámetros son la cifra que decide si se ajusta en una tarjeta gráfica dada.
- Parámetros
- 1B
- Datos de entrenamiento
- 1,200,000,000,000 tokens
"To answer this, we train a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200x200 pixel images downloaded from the Internet)"
10 million 200x200 images extracted from Youtube videos
Entrenamiento de computación
La aritmética realizada para entrenar el modelo, medida en operaciones de punto flotante. Es una medida de lo que costó la ejecución del entrenamiento, no de cuán rápido responde el modelo terminado.
- Entrenamiento de computación
- 6 × 10¹⁷ FLOP
- Cómo se estableció
- Operation counting
Assuming 1 epoch, 10 million images and 1 billion parameters, 6*N*D = 6*10^17 FLOP
El entrenamiento
Lo que se necesitó físicamente para entrenar: qué chips, cuántos, durante cuánto tiempo y qué consumió de la pared.
- Tiempo en tiempo real
- 72 hours
- Costo de computación
- $16
"We train this network using model parallelism and asynchronous SGD on a cluster with 1,000 machines (16,000 cores) for three days. "
Cómo se clasifica
Etiquetas que aplica el conjunto de datos de origen al rastrear modelos notables y cuán seguro está de la entrada.
- Frontier model
- Yes
- Por qué se rastrea
- Highly cited,SOTA improvement
- Registrar confianza
- Likely
- Citas
- 2,909
"we trained our network to obtain 15.8% accuracy in recognizing 20,000 object categories from ImageNet, a leap of 70% relative improvement over the previous state-of-the-art"
Fuentes
De dónde proviene este registro y cuándo fue revisado por última vez.
- Referencia
- Building High-level Features Using Large Scale Unsupervised Learning
- Última actualización
- 28 November 2025
Qué significan los números
Acerca de este modelo
Unsupervised High-level Feature Learner was published by Google, in United States of America, in July 2012. industry is the category the publisher falls under.
It works in Vision, and is recorded as doing image classification.
Debido a que los pesos no están disponibles, ninguna de las cifras de hardware en otro lugar de este sitio se aplica a él.
Training and provenance
Training it took roughly 6 × 10¹⁷ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
The training set ran to roughly 1,200,000,000,000 tokens.
Its inclusion criterion is highly cited,SOTA improvement.
Respuestas
Unsupervised High-level Feature Learner — Preguntas frecuentes
What is Unsupervised High-level Feature Learner used for?
Unsupervised High-level Feature Learner works in Vision, and is recorded as handling image classification. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
How much compute was used to train Unsupervised High-level Feature Learner?
Around 6 × 10¹⁷ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run Unsupervised High-level Feature Learner?
None. Unsupervised High-level Feature Learner is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Unsupervised High-level Feature Learner open source?
The licensing for Unsupervised High-level Feature Learner was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Unsupervised High-level Feature Learner have?
Unsupervised High-level Feature Learner has 1B parameters. "To answer this, we train a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200x200 pixel images downloaded from the Internet)". That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Unsupervised High-level Feature Learner?
Unsupervised High-level Feature Learner was published by Google, based in United States of America, categorised as industry.
When was Unsupervised High-level Feature Learner released?
Unsupervised High-level Feature Learner was published in July 2012. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
La otra dirección
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