… are adapted, rather it is the distance between them. Thus, after the learning process the
neurons represent a mapping of … A supervised, edge adaptive map can be used to learn lower …

… grayscale edge map, but cannot determine the precise position of the edge; the adaptive …
, we propose a weakly supervised detection method based on edge mapping and SDAE. It …

… tampering Edge-based Class Activation Map (… supervised approach, relying solely on
image-level annotations. Ultimately, we establish a fundamental benchmark for weakly-supervised …

… However, the differences in nodule size in thyroid ultrasound images and the limitations of
class activation maps in weakly supervised segmentation methods typically lead to under- and…

… maps. A loss function composed of saliency supervision, switch map supervision, and
edgepreserving constraints are designed to make full supervision, and the entire network is …

… , we proposed an adaptive guidance strategy, … maps to different treat mural feature maps
affected by disease and those that are not. Finally, we employ a depth prediction supervision …

… adaptive fusion is extracted from the variance map of denoised texture image. The second
step is to take advantage of curvelet denoising for the edges … A variance map of the denoised …

… highly adaptive and there are no parameters to tune. We show applications for edge detection
… Since the labeling was error prone we smoothed the binary edge map using a Gaussian …

… the edge of the defocused blur region, this study develops an adaptive multi-supervision
network … Meanwhile, three types of mapping are used to complete the supervised training of the …

… map with the re-estimated parameters as the GT. For the individuals, we introduce the
supervision to … Based on the analysis above, we have designed an adaptive dilated convolution …