Adversarial Joint Adaptation Network (ResNet)
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Tsinghua University,University of California (UC) Berkeley
- Organisation type
- Academia,Academia
- Country
- China, United States of America
- Published
- 17 August 2017
- Authors
- Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
- Numerical format
- FP32
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 60M
- Training data
- 4,652 tokens
Model is based on ResNet (60m params), might have more parameters though "We implement all deep methods based on the Caffe framework, and fine-tune from Caffe-provided models of AlexNet (Krizhevsky et al., 2012) and ResNet (He et al., 2016), both are pre-trained on the ImageNet 2012 dataset."
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
- Citations
- 2,743
Sources
Where this record came from and when it was last checked.
- Reference
- Deep Transfer Learning with Joint Adaptation Networks
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Adversarial Joint Adaptation Network (ResNet) was published by Tsinghua University,University of California (UC) Berkeley, in the country recorded as China, during August 2017. The category the publisher falls under is academia,Academia.
It works in the domain of Vision, and is recorded as performing the task of image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
It was trained on a corpus of about 4,652 tokens of text.
Answers
Adversarial Joint Adaptation Network (ResNet) — common questions
Adversarial Joint Adaptation Network (ResNet)— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Adversarial Joint Adaptation Network (ResNet)— what GPU do I need to run it?
None. This 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.
Adversarial Joint Adaptation Network (ResNet)— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Adversarial Joint Adaptation Network (ResNet)— how many parameters does it have?
It has a parameter count of 60M. Model is based on ResNet (60m params), might have more parameters though "We implement all deep methods based on the Caffe framework, and fine-tune from Caffe-provided models of AlexNet (Krizhevsky et al., 2012) and ResNet (He et al., 2016), both are pre-trained on the ImageNet 2012 dataset.". 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.
Adversarial Joint Adaptation Network (ResNet)— who created it?
It was published by Tsinghua University,University of California (UC) Berkeley, based in China, an organisation categorised as academia,Academia.
Adversarial Joint Adaptation Network (ResNet)— when was it released?
It was published in August 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.