This work addresses the long-standing problem of robust event localization in the presence of temporally misaligned labels in the training data. We propose a novel versatile loss function that generalizes a number of training regimes from standard fully-supervised cross-entropy to count-based weakly-supervised learning. Unlike classical models which are constrained to strictly fit the annotations during training, our soft localization learning approach relaxes the reliance on the exact position of labels instead. Training with this new loss function exhibits strong robustness to temporal misalignment of labels, thus alleviating the burden of precise annotation of temporal sequences. We demonstrate state-of-the-art performance against standard benchmarks in a number of challenging experiments and further show that robustness to label noise is not achieved at the expense of raw performance.
This paper deals with temporal label noise, or label misalignment problem in localization, which is the long-standing problem of robust event localization regarding the temporally misaligned labels in sequential data.  Some existing works are constrained to hardly fit the annotations of data during training. The work models the temporal label misalignment with perturbations to the ground-truth timestamps, and presents a soft localization learning scheme which relaxes the reliance on the exact position of labels. The idea introduced to temporal data is well-motivated since the label for temporal data is often dispersed. Technically speaking, the proposed SoftLoc loss comprises two terms: 1). $\mathscr{L}_{SLL}$: a soft learning loss that relaxes the prediction mass concentration, by symmetrically filtering the labels and predictions. This term helps to relax the model’s reliance on exact label locations. 2). $\mathscr{L}_{MC}$: a mass convergence loss that acts as a regularizer to facilitate the model with precise impulse-like localizations. With a trade of factor to balance two terms, the SoftLoc model can achieve precise impulse-like localization performance without weakening the model robustness. Various applications, such as PIANO ONSET, DRUM DETECTION and TIME SERIES DETECTION are performed to verify the effectiveness of the proposed method. And state-of-the-art performance is achieved.
Despite the achievement indicated by the experiments, I still have some concerns about this paper.
(1)	The authors propose the relaxed loss L_{SLL} for the soft learning of the location. And the label smoothing idea (applying a ˜S^2-Gaussian filter to the labels) has been introduced to increase the robustness to temporal misalignment of annotations [2]. Although the authors discuss the several inherent drawbacks of it in the 2nd paragraph of Related Work, they still follow the label smoothing idea in their model (Eq.2) in Section 4.1 in a two-side smoothed process. The reason should be clarified.
(2)	The authors claim that their method has the advantage of generalizing some regimes of weakly-supervised learning. And the adopted L_{MC} is a weakly-supervised loss. Does the advantage come from L_{MC}?   
(3)	The authors propose the two-side relaxed loss L_{SLL} for the soft learning of temporal localization problem. However, in the ablation study, the authors do not give the results with different level of label noise for one-side variant. Adding these results could help to demonstrate the necessity of the two-side relaxed loss L_{SLL}. 
References:
[1] Improved musical onset detection with convolutional neural networks
[2] Onsets and frames: Dual-objective piano transcription