  Image classification is a fundamental task in computer vision, and the quest
to enhance DNN accuracy without inflating model size or latency remains a
pressing concern. We make a couple of advances in this regard, leading to a
novel EncodeNet design and training framework. The first advancement involves
Converting Autoencoders, a novel approach that transforms images into an
easy-to-classify image of its class. Our prior work that applied the Converting
Autoencoder and a simple classifier in tandem achieved moderate accuracy over
simple datasets, such as MNIST and FMNIST. However, on more complex datasets
like CIFAR-10, the Converting Autoencoder has a large reconstruction loss,
making it unsuitable for enhancing DNN accuracy. To address these limitations,
we generalize the design of Converting Autoencoders by leveraging a larger
class of DNNs, those with architectures comprising feature extraction layers
followed by classification layers. We incorporate a generalized algorithmic
design of the Converting Autoencoder and intraclass clustering to identify
representative images, leading to optimized image feature learning. Next, we
demonstrate the effectiveness of our EncodeNet design and training framework,
improving the accuracy of well-trained baseline DNNs while maintaining the
overall model size. EncodeNet's building blocks comprise the trained encoder
from our generalized Converting Autoencoders transferring knowledge to a
lightweight classifier network - also extracted from the baseline DNN. Our
experimental results demonstrate that EncodeNet improves the accuracy of VGG16
from 92.64% to 94.05% on CIFAR-10 and RestNet20 from 74.56% to 76.04% on
CIFAR-100. It outperforms state-of-the-art techniques that rely on knowledge
distillation and attention mechanisms, delivering higher accuracy for models of
comparable size.
