Beyond Supervised Representation Learning

Noroozi, Mehdi (2019). Beyond Supervised Representation Learning. (Dissertation, Universität Bern, Philosophisch-naturwissenschaftliche Fakultät)

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The complexity of any information processing task is highly dependent on the space where data is represented. Unfortunately, pixel space is not appropriate for the computer vision tasks such as object classification. The traditional computer vision approaches involve a multi-stage pipeline where at first images are transformed to a feature space through a handcrafted function and then consequenced by the solution in the feature space. The challenge with this approach is the complexity of designing handcrafted functions that extract robust features. The deep learning based approaches address this issue by end-to-end training of a neural network for some tasks that lets the network to discover the appropriate representation for the training tasks automatically. It turns out that image classification task on large scale annotated datasets yields a representation transferable to other computer vision tasks. However, supervised representation learning is limited to annotations. In this thesis we study self-supervised representation learning where the goal is to alleviate these limitations by substituting the classification task with pseudo tasks where the labels come for free.
We discuss self-supervised learning by solving jigsaw puzzles that uses context as supervisory signal. The rational behind this task is that the network requires to extract features about object parts and their spatial configurations to solve the jigsaw puzzles.
We also discuss a method for representation learning that uses an artificial supervisory signal based on counting visual primitives. This supervisory signal is obtained from an equivariance relation. We use two image transformations in the context of counting: scaling and tiling. The first transformation exploits the fact that the number of visual primitives should be invariant to scale. The second transformation allows us to equate the total number of visual primitives in each tile to that in the whole image.
The most effective transfer strategy is fine-tuning, which restricts one to use the same model or parts thereof for both pretext and target tasks. We discuss a novel framework for self-supervised learning that overcomes limitations in designing and comparing different tasks, models, and data domains. In particular, our framework decouples the structure of the self-supervised model from the final task-specific fine-tuned model.
Finally, we study the problem of multi-task representation learning. A naive approach to enhance the representation learned by a task is to train the task jointly with other tasks that capture orthogonal attributes. Having a diverse set of auxiliary tasks, imposes challenges on multi-task training from scratch. We propose a framework that allows us to combine arbitrarily different feature spaces into a single deep neural network. We reduce the auxiliary tasks to classification tasks and the multi-task learning to multi-label classification task consequently. Nevertheless, combining multiple representation space without being aware of the target task might be suboptimal. As our second contribution, we show empirically that this is indeed the case and propose to combine multiple tasks after the fine-tuning on the target task.

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