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An additional difficulty in the case where such small sub-regions are used arises in the case of sparsely labelled samples for which some sub-regions might correspond to no-signal areas in the sample: in this case, no measurement can be performed in the corresponding sub-region.
As earlier works have shown the alternative of ground truth evaluation is limited to global statistics [8] over sparsely labelled ground truth (e.g. [11, 12]).
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A similar pattern of large and spatially clustered AP-labeled arbors was observed in other sparsely labeled ChAT-IRES-CreER Z/AP brains (Figure 6).
No matter networks are sparsely labeled or densely labeled, our approach usually provides higher metric scores than competing methods.
The results indicate that our (text {IDRN}_c) outperforms most baseline methods when networks are sparsely labeled.
We also note that when the networks are sparsely labeled (i.e., with 10 or 20% labeled data), (text {IDRN}_c) performs slightly better than IDRN.
In addition, the framework is designed to work on imbalanced and sparsely labeled data streams, as a limited-memory, incremental algorithm.
Ali et al. [37] presented an approach that learned a specific model by propagating a sparsely labeled training video based on object tracking.
The transductive inference paradigm allows us to reduce the inference error for the given set of unlabeled data, as sparsely labeled pixels are learned by accounting for both labeled and unlabeled information.
To study the performance of different classification algorithms on a sparsely labeled network, we show results obtained by using 10% nodes for training and the left 90% nodes for testing.
In this study, we present a tripartite system to noninvasively label entire processes and presynaptic compartments of sparsely labeled individual neurons with temporal and spatial control.
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