Top-1 Accuracy(5-Way-1-Shot): 76.17 +- 0.41
Papers with Code · CC BY-SA 4.0
Few-Shot Audio Classification
Few-shot classification for audio signals. Presents a unique challenge compared to other few-shot domains as we deal with temporal dependencies as well.
Like other few-shot problems, few-shot audio classification can be tackled in a variety of ways, from using supervised meta-learning on the same primary dataset, to pre-training on an external dataset and utilising linear readout. For this reason, results in each dataset leaderboard should be correctly tagged e.g. with "Within Dataset Meta-Learning" etc
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Dataset기록된 모델 예시MetricPaperCode
MT-SLVR (SimCLR + MLAP) w/ Parallel Adapters (FSD50K, RN18)
Top-1 Accuracy(5-Way-1-Shot): 59.49±0.42
논문
코드
MT-SLVR (SimCLR + MLAP) w/ Parallel Adapters (FSD50K, RN18)
Top-1 Accuracy(5-Way-1-Shot): 29.61±0.38
논문
코드
MT-SLVR (SimCLR + MLAP) w/ Parallel Adapters (FSD50K, RN18)
Top-1 Accuracy(5-Way-1-Shot): 35.22±0.40
논문
코드
데이터셋
10개논문
Top-1 Accuracy(5-Way-1-Shot): 69.53±0.39
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