Masked Autoencoders (MAEs) learn rich low-level representations from
unlabeled data but require substantial labeled data to effectively adapt to
downstream tasks. Conversely, Instance Discrimination (ID) emphasizes
high-level semantics, offering a potential solution to alleviate annotation
requirements in MAEs. Although combining these two approaches can address
downstream tasks with limited labeled data, naively integrating ID into MAEs
leads to extended training times and high computational costs. To address this
challenge, we introduce uaMix-MAE, an efficient ID tuning strategy that
leverages unsupervised audio mixtures. Utilizing contrastive tuning, uaMix-MAE
aligns the representations of pretrained MAEs, thereby facilitating effective
adaptation to task-specific semantics. To optimize the model with small amounts
of unlabeled data, we propose an audio mixing technique that manipulates audio
samples in both input and virtual label spaces. Experiments in low/few-shot
settings demonstrate that \modelname achieves 4-6% accuracy improvements over
various benchmarks when tuned with limited unlabeled data, such as
AudioSet-20K. Code is available at this https URL

提出了一种有效的 ID 调节策略 uaMix-MAE，通过对预训练的 MAEs 进行对比调节，以实现对特定任务的语义有效适应，并且通过操纵输入和虚拟标签空间中的音频样本来优化模型，实验结果表明在低 / 少样本设置中，所提出的模型在有限的无标签数据下相比不同的基准模型（如 AudioSet-20K）取得了 4-6% 的准确率提升。