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Few-Shot Audio Classification

Few-Shot Learning › Few-Shot Audio Classification 67 평가 기록 10 datasets

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

개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.

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Dataset기록된 모델 예시MetricPaperCode
Meta-Curvature (CRNN)
Top-1 Accuracy(5-Way-1-Shot): 61.34 +- 0.46
논문 코드
Meta-Curvature (CRNN)
Top-1 Accuracy(5-Way-1-Shot): 76.17 +- 0.41
논문 코드
MAML (CRNN)
Top-1 Accuracy(5-Way-1-Shot): 43.45 +- 0.46
논문 코드
Meta-Curvature (CRNN)
Top-1 Accuracy(5-Way-1-Shot): 96.47 +-0.19
논문 코드
Meta-Curvature (CRNN)
Top-1 Accuracy(5-Way-1-Shot): 63.85 +- 0.44
논문 코드
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
MT-SLVR (SimCLR + MLAP) w/ Parallel Adapters (FSD50K, RN18)
Top-1 Accuracy(5-Way-1-Shot): 28.92±0.37
논문 코드
SimCLR (FSD50K, RN18)
Top-1 Accuracy(5-Way-1-Shot): 25.68±0.35
논문 코드

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