The recent developments in the research area indicate a significant shift towards enhancing the robustness and generalizability of machine learning models, particularly in the face of biases and noise. There is a growing emphasis on unsupervised and self-distillation methods for mitigating biases in neural networks, which aim to improve model performance without relying on human annotations. These approaches are designed to transfer knowledge within the network to create more robust representations that generalize across diverse datasets and biases. Additionally, the field is exploring the nuanced effects of curriculum learning, particularly focusing on the robustness and impact of different scoring functions for sample difficulty estimation. The research highlights the importance of the ordering in which data is presented for curriculum-based training and the potential for complementary models trained with different strategies. Furthermore, there is a notable investigation into the impact of label noise on learning complex features, demonstrating that pre-training with noisy labels can encourage models to learn more diverse and complex features without compromising performance. These advancements collectively push the boundaries of model adaptability and performance in real-world scenarios.