  Large occlusions result in a significant decline in image classification
accuracy. During inference, diverse types of unseen occlusions introduce
out-of-distribution data to the classification model, leading to accuracy
dropping as low as 50%. As occlusions encompass spatially connected regions,
conventional methods involving feature reconstruction are inadequate for
enhancing classification performance. We introduce LEARN: Latent Enhancing
feAture Reconstruction Network -- An auto-encoder based network that can be
incorporated into the classification model before its classifier head without
modifying the weights of classification model. In addition to reconstruction
and classification losses, training of LEARN effectively combines intra- and
inter-class losses calculated over its latent space -- which lead to
improvement in recovering latent space of occluded data, while preserving its
class-specific discriminative information. On the OccludedPASCAL3D+ dataset,
the proposed LEARN outperforms standard classification models (VGG16 and
ResNet-50) by a large margin and up to 2% over state-of-the-art methods. In
cross-dataset testing, our method improves the average classification accuracy
by more than 5% over the state-of-the-art methods. In every experiment, our
model consistently maintains excellent accuracy on in-distribution data.
