Comparison of self-supervised in-domain and supervised out-domain transfer learning for bird species recognition

• URL: http://arxiv.org/abs/2404.17252v1
• Date: Fri, 26 Apr 2024 08:47:28 GMT
• Title: Comparison of self-supervised in-domain and supervised out-domain transfer learning for bird species recognition
• Authors: Houtan Ghaffari, Paul Devos,
• Abstract summary: Transferring the weights of a pre-trained model to assist another task has become a crucial part of modern deep learning. Our experiments will demonstrate the usefulness of in-domain models and datasets for bird species recognition.
• Score: 0.19183348587701113
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Transferring the weights of a pre-trained model to assist another task has become a crucial part of modern deep learning, particularly in data-scarce scenarios. Pre-training refers to the initial step of training models outside the current task of interest, typically on another dataset. It can be done via supervised models using human-annotated datasets or self-supervised models trained on unlabeled datasets. In both cases, many pre-trained models are available to fine-tune for the task of interest. Interestingly, research has shown that pre-trained models from ImageNet can be helpful for audio tasks despite being trained on image datasets. Hence, it's unclear whether in-domain models would be advantageous compared to competent out-domain models, such as convolutional neural networks from ImageNet. Our experiments will demonstrate the usefulness of in-domain models and datasets for bird species recognition by leveraging VICReg, a recent and powerful self-supervised method.

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