The recent advancements in self-supervised learning (SSL) have shown significant promise in various domains, particularly in fine-grained recognition and zero-shot learning. The field is witnessing a shift towards more sophisticated methods that address the limitations of traditional SSL by incorporating generative models and relation-aware meta-learning. These innovations aim to enhance the generalization capabilities of models, enabling them to perform effectively on unseen categories and fine-grained distinctions. Additionally, there is a growing emphasis on the integration of uncertainty representation and multi-level correlation networks to improve the robustness and accuracy of few-shot image classification. The use of generative self-augmentation techniques and equivariant representation learning through image reconstruction is also gaining traction, offering new avenues for improving the generalizability of SSL models. Notably, these developments are not only advancing the theoretical underpinnings of SSL but also demonstrating practical improvements across diverse datasets and downstream tasks.