Papers › TransformNet: Self-supervised representation learning through predicting geometric...

TransformNet: Self-supervised representation learning through predicting geometric transformations

8 Feb 2022arXiv:2202.04181archive 2025-07-28

Sayed Hashim, Muhammad Ali

Deep neural networks need a big amount of training data, while in the real world there is a scarcity of data available for training purposes. To resolve this issue unsupervised methods are used for training with limited data. In this report, we describe the unsupervised semantic feature learning approach for recognition of the geometric transformation applied to the input data. The basic concept of our approach is that if someone is unaware of the objects in the images, he/she would not be able to quantitatively predict the geometric transformation that was applied to them. This self supervised scheme is based on pretext task and the downstream task. The pretext classification task to quantify the geometric transformations should force the CNN to learn high-level salient features of objects useful for image classification. In our baseline model, we define image rotations by multiples of 90 degrees. The CNN trained on this pretext task will be used for the classification of images in the CIFAR-10 dataset as a downstream task. we run the baseline method using various models, including ResNet, DenseNet, VGG-16, and NIN with a varied number of rotations in feature extracting and fine-tuning settings. In extension of this baseline model we experiment with transformations other than rotation in pretext task. We compare performance of selected models in various settings with different transformations applied to images,various data augmentation techniques as well as using different optimizers. This series of different type of experiments will help us demonstrate the recognition accuracy of our self-supervised model when applied to a downstream task of classification.

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Data Augmentation Image Classification Representation Learning image-classification

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1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Concatenated Skip Connection Convolution Dense Block Dense Connections Dropout Global Average Pooling Kaiming Initialization Max Pooling ReLU Residual Block Residual Connection Softmax

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