**Sample Consistency Network (SCNet)** is a method for instance segmentation which ensures the IoU distribution of the samples at training time are as close to that at inference time. To this end, only the outputs of the last box stage are used for mask predictions at both training and inference. The Figure shows the IoU distribution of the samples going to the mask branch at training time with/without sample consistency compared to that at inference time.

The  overhead  cost  of  training  multiple  deep  neural networks  could  be  very  high  in  terms  of  the  training  time, hardware, and computational resource requirement and often acts  as  obstacle  for  creating  deep  ensembles.  To  overcome these barriers Huang et al. proposed a unique method to create  ensemble  which  at  the  cost  of  training  one  model, yields  multiple  constituent  model  snapshots  that  can  be ensembled together to create a strong learner. The core idea behind the concept is to make the model converge to several local minima along the optimization path and save the model parameters at these local minima points. During the training phase, a neural network would traverse through many such points. The lowest of all such local minima is known as the Global Minima. The larger the model, more are the number of parameters and larger the number of local minima points. This implies, there are discrete sets of weights and biases, at which  the  model  is  making  fewer  errors.  So,  every  such minimum  can  be  considered a  weak  but  a  potential learner model for the problem being solved. Multiple such snapshot of  weights  and  biases  are  recorded  which  can  later  be ensembled to get a better generalized model which makes the least amount of mistakes.

Snapshot Ensembles

Snapshot Ensembles: Train 1, get M for free

SCNet

SCNet: Training Inference Sample Consistency for Instance Segmentation

**Spatial Group-wise Enhance** is a module for convolutional neural networks that can adjust the
importance of each sub-feature by generating an attention factor for each spatial location in each semantic group, so that every individual group can autonomously enhance its learnt expression and suppress possible noise

Inside each feature group, we model a spatial enhance mechanism inside each feature group, by scaling the feature vectors over all the locations with an attention mask. This attention mask is designed to suppress the possible noise and highlight the correct semantic feature regions. Different from other popular attention methods, it utilises the similarity between the global statistical feature and the local ones of each location as the source of generation for the attention masks.

Source	SCNet: Training Inference Sample Consistency for Instance Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com