In the realm of music information retrieval, similarity-based retrieval and
auto-tagging serve as essential components. Given the limitations and
non-scalability of human supervision signals, it becomes crucial for models to
learn from alternative sources to enhance their performance. Self-supervised
learning, which exclusively relies on learning signals derived from music audio
data, has demonstrated its efficacy in the context of auto-tagging. In this
study, we propose a model that builds on the self-supervised learning approach
to address the similarity-based retrieval challenge by introducing our method
of metric learning with a self-supervised auxiliary loss. Furthermore,
diverging from conventional self-supervised learning methodologies, we
discovered the advantages of concurrently training the model with both
self-supervision and supervision signals, without freezing pre-trained models.
We also found that refraining from employing augmentation during the
fine-tuning phase yields better results. Our experimental results confirm that
the proposed methodology enhances retrieval and tagging performance metrics in
two distinct scenarios: one where human-annotated tags are consistently
available for all music tracks, and another where such tags are accessible only
for a subset of tracks.

本研究提出了一种基于自我监督学习方法的模型，通过介绍自我监督辅助损失的度量学习方法来解决基于相似性的检索挑战。实验结果表明，该方法可以增强检索和标记性能指标，可用于所有音乐曲目和部分音乐曲目的两种情况。

音乐相似度检索和自动标记中度量学习的自监督辅助损失

Self-supervised Auxiliary Loss for Metric Learning in Music  Similarity-based Retrieval and Auto-tagging

Online educational platforms organize academic questions based on a
hierarchical learning taxonomy (subject-chapter-topic). Automatically tagging
new questions with existing taxonomy will help organize these questions into
different classes of hierarchical taxonomy so that they can be searched based
on the facets like chapter. This task can be formulated as a flat multi-class
classification problem. Usually, flat classification based methods ignore the
semantic relatedness between the terms in the hierarchical taxonomy and the
questions. Some traditional methods also suffer from the class imbalance issues
as they consider only the leaf nodes ignoring the hierarchy. Hence, we
formulate the problem as a similarity-based retrieval task where we optimize
the semantic relatedness between the taxonomy and the questions. We demonstrate
that our method helps to handle the unseen labels and hence can be used for
taxonomy tagging in the wild. In this method, we augment the question with its
corresponding answer to capture more semantic information and then align the
question-answer pair's contextualized embedding with the corresponding label
(taxonomy) vector representations. The representations are aligned by
fine-tuning a transformer based model with a loss function that is a
combination of the cosine similarity and hinge rank loss. The loss function
maximizes the similarity between the question-answer pair and the correct label
representations and minimizes the similarity to unrelated labels. Finally, we
perform experiments on two real-world datasets. We show that the proposed
learning method outperforms representations learned using the multi-class
classification method and other state of the art methods by 6% as measured by
Recall@k. We also demonstrate the performance of the proposed method on unseen
but related learning content like the learning objectives without re-training
the network.

该研究提出了一种基于相似性检索的方法来对在线教育平台中的学术问题进行分类，将问题隶属于不同的学科、章节和主题，实现分类的目的。通过 Fine-tuning 一种基于 transformer 的模型，并使用余弦相似性和 Hinge rank loss 的组合损失函数进行优化，该方法比其他基于多类分类方法和现有技术的性能更好，能够处理未见标签的分类问题，具有更广泛的适用性。