  Self-supervised learning (SSL) has become a popular method for generating
invariant representations without the need for human annotations. Nonetheless,
the desired invariant representation is achieved by utilising prior online
transformation functions on the input data. As a result, each SSL framework is
customised for a particular data type, e.g., visual data, and further
modifications are required if it is used for other dataset types. On the other
hand, autoencoder (AE), which is a generic and widely applicable framework,
mainly focuses on dimension reduction and is not suited for learning invariant
representation. This paper proposes a generic SSL framework based on a
constrained self-labelling assignment process that prevents degenerate
solutions. Specifically, the prior transformation functions are replaced with a
self-transformation mechanism, derived through an unsupervised training process
of adversarial training, for imposing invariant representations. Via the
self-transformation mechanism, pairs of augmented instances can be generated
from the same input data. Finally, a training objective based on contrastive
learning is designed by leveraging both the self-labelling assignment and the
self-transformation mechanism. Despite the fact that the self-transformation
process is very generic, the proposed training strategy outperforms a majority
of state-of-the-art representation learning methods based on AE structures. To
validate the performance of our method, we conduct experiments on four types of
data, namely visual, audio, text, and mass spectrometry data, and compare them
in terms of four quantitative metrics. Our comparison results indicate that the
proposed method demonstrate robustness and successfully identify patterns
within the datasets.
