Title: OUT-OF-DISTRIBUTION CLASSIFICATION WITH ADAPTIVE LEARNING OF LOW-LEVEL CONTEXTUAL FEATURES. Abstract: Humans can inherently identify what is unknown to them, but the existing Neural Network (NN) is still lacking in this aspect. Out-of-Distribution (OOD) classification is an incredibly challenging problem for any NN model. In General, any model tries to predict the OOD samples from the labels used for training only, but that is not acceptable for AGI (Artificial General Intelligence) [Fjelland(2020)]. There are several kinds of research already done to avoid this issue and build a model to predict the OOD samples, existing baseline work like 1) Thresholding SoftMax, 2) train model by adding extra OOD class as a label, or 3) Mahalanobis distance-based approach. All existing approach uses the CNN to get the spatial feature information and channel-wise information within the local receptive field at each layer. Here in this paper, we have proposed a method to learn the features of In-class and OOD sample’s features with global receptive field among channels to learn the spatial relationship with modified SEnet block. Broadly, our model learns the interdependencies between channels with adaptive recalibration of the weights of stacked channels at each layer. To give more weightage to the In-class samples, we uniformly normalized the OOD samples with the total number of known class samples and trained our model to suppress the OOD class probability with a simple and effective loss function. We did our experiments for our model with MNIST and F-MNIST as In-class samples and EMNIST, KMNIST, not-MNIST, Omniglot, Uniform Noise, Gaussian Noise as OOD samples.