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--- |
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library_name: keras |
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tags: |
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- semi-supervised |
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- image classification |
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- domain adaption |
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datasets: |
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- MNIST |
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- SVHN |
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--- |
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## Model description |
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This is an image classification model based on a [WideResNet-2-28](https://arxiv.org/abs/1605.07146v4), trained using the [AdaMatch](https://arxiv.org/abs/2106.04732) method by Berthelot et al. |
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The training was based on the example [Semi-supervision and domain adaptation with AdaMatch]('https://keras.io/examples/vision/adamatch/') on keras.io by [Sayak Paul](https://twitter.com/RisingSayak). |
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The main difference to the training in the keras.io example is that here I increased the number of Epochs to 30, for a better target dataset performance. |
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## Intended uses & limitations |
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AdaMatch attempts to combine *semi-supervised learning*, i.e. learning with a partially labelled dataset and *unsupersived domain adaption*, i.e. adapting a model to a different domain dataset without any labels. |
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So it actually performs **semi-supervised domain adaptation (SSDA)**. |
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The model is inteded to show that AdaMatch is able to carry out SSDA, with a accuracy on the target domain (SVHN) that is exceeding or competitive with other methods. |
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### Limitations |
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The model was trained on MNIST as source and SVHN as target dataset. Thus, the classification performance on MNIST is very good (98.46%), while the accuracy on SVHN is "only" at 26.51%. Compared to the training of the same architecture without AdaMatch, this still is about 17% better |
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## Training and evaluation data |
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### Training Data |
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The model was trained using the [MNIST](https://huggingface.co/datasets/mnist) (as source domain) and [SVHN cropped](http://ufldl.stanford.edu/housenumbers/) (as target domain) datasets. For training the images were used at a resolution of (32,32,3). |
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Augmented versions of the source and target data were created in two versions - weakly and strongly augmented, as written in the original paper. |
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### Training Procedure |
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This image from the original paper shows the workflow of AdaMatch: |
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For more information, refer to the [paper](https://arxiv.org/abs/2106.04732) or the original example at [keras.io]('https://keras.io/examples/vision/adamatch/'). |
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### Hyperparameters |
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The following hyperparameters were used during training: |
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- Epochs: 30 |
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- Source Batch Size: 64 |
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- Target Batch Size: 3 * 64 |
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- Learning Rate: 0.03 |
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- Weight Decay: 0.0005 |
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- Network Depth: 28 |
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- Network Width Multiplier = 2 |
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## Evaluation |
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Accuracy on **source** test set: **98.46%** |
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Accuracy on **target** test set: **26.51%** |
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