Self-supervised learning (SSL) allows training data representations without a supervised signal and has become an important paradigm in machine learning. Most SSL methods employ the cosine similarity between embedding vectors and hence effectively embed data on a hypersphere. While this seemingly implies that embedding norms cannot play any role in SSL, a few recent works have suggested that embedding norms have properties related to network convergence and confidence. In this paper, we resolve this apparent contradiction and systematically establish the embedding norm's role in SSL training. Using theoretical analysis, simulations, and experiments, we show that embedding norms (i) govern SSL convergence rates and (ii) encode network confidence, with smaller norms corresponding to unexpected samples. Additionally, we show that manipulating embedding norms can have large effects on convergence speed. Our findings demonstrate that SSL embedding norms are integral to understanding and optimizing network behavior.

Self-supervised learning (SSL) methods are popular since they can address situations with limited annotated data by directly utilising the underlying data distribution. However, the adoption of such methods is not explored enough in ultrasound (US) imaging, especially for fetal assessment. We investigate the potential of dual-encoder SSL in utilizing unlabelled US video data to improve the performance of challenging downstream Standard Fetal Cardiac Planes (SFCP) classification using limited labelled 2D US images. We study 7 SSL approaches based on reconstruction, contrastive loss, distillation, and information theory and evaluate them extensively on a large private US dataset. Our observations and findings are consolidated from more than 500 downstream training experiments under different settings. Our primary observation shows that for SSL training, the variance of the dataset is more crucial than its size because it allows the model to learn generalisable representations, which improve the performance of downstream tasks. Overall, the BarlowTwins method shows robust performance, irrespective of the training settings and data variations, when used as an initialisation for downstream tasks. Notably, full fine-tuning with 1% of labelled data outperforms ImageNet initialisation by 12% in F1-score and outperforms other SSL initialisations by at least 4% in F1-score, thus making it a promising candidate for transfer learning from US video to image data.

Self-supervised learning (SSL) has emerged as a promising solution for addressing the challenge of limited labeled data in deep neural networks (DNNs), offering scalability potential. However, the impact of design dependencies within the SSL framework remains insufficiently investigated. In this study, we comprehensively explore SSL behavior across a spectrum of augmentations, revealing their crucial role in shaping SSL model performance and learning mechanisms. Leveraging these insights, we propose a novel learning approach that integrates prior knowledge, with the aim of curtailing the need for extensive data augmentations and thereby amplifying the efficacy of learned representations. Notably, our findings underscore that SSL models imbued with prior knowledge exhibit reduced texture bias, diminished reliance on shortcuts and augmentations, and improved robustness against both natural and adversarial corruptions. These findings not only illuminate a new direction in SSL research, but also pave the way for enhancing DNN performance while concurrently alleviating the imperative for intensive data augmentation, thereby enhancing scalability and realworld problem-solving capabilities. Deep neural networks (DNNs) have proven to be highly effective in encoding patterns in data distribution to produce powerful and rich representations that have improved generalization performance across various perception tasks, such as classification, detection, and segmentation. However, one of the major limitations is that DNNs are data-hungry and annotating millions of available data is expensive. Self-supervised learning (SSL) has been proposed as a promising solution to this issue, to enable the learning of useful representations without manual annotations. Self-supervised learning paradigm needs to ensure that the resulting features are generic to be applicable to a wide range of real-world applications. Various SSL methods, including pretext-based (Gidaris Figure 1: The impact of augmentations on SSL methods is et al., 2018; Noroozi & Favaro, 2016), critical: as removing strong augmentations from SSL training contrastive-based (Chen et al., 2020a; He et al., can result in a significant drop in their performance.

Active Learning (AL) and Semi-supervised Learning are two techniques that have been studied to reduce the high cost of deep learning by using a small amount of labeled data and a large amount of unlabeled data. To improve the accuracy of models at a lower cost, we propose a method called Active Semi-supervised Learning (ASSL), which combines AL and SSL. To maximize the synergy between AL and SSL, we focused on the differences between ASSL and AL. ASSL involves more dynamic model updates than AL due to the use of unlabeled data in the training process, resulting in the temporal instability of the predicted probabilities of the unlabeled data. This makes it difficult to determine the true uncertainty of the unlabeled data in ASSL. To address this, we adopted techniques such as exponential moving average (EMA) and upper confidence bound (UCB) used in reinforcement learning. Additionally, we analyzed the effect of label noise on unsupervised learning by using weak and strong augmentation pairs to address datainconsistency. By considering both uncertainty and datainconsistency, we acquired data samples that were used in the proposed ASSL method. Our experiments showed that ASSL achieved about 5.3 times higher computational efficiency than SSL while achieving the same performance, and it outperformed the state-of-the-art AL method.