## 1 Introduction
Compressive sensing (CS) [1; 2; 3; 4; 5; 6; 7; 8] is a new theory that works beyond the standard technique and demonstrates how a sparse signal can be reconstructed from a smaller number of incoherent samples. Comprehensive reviews of the present state of the field of CS imaging have been published. The single-pixel camera (SPC) [7] is particularly helpful in imaging outside the visible range, such as infrared imaging, terahertz imaging, and hyperspectral imaging, when detector arrays are either prohibitively costly or non-existent. Instead of rebuilding the whole picture, the aim in many applications is to tackle an inference issue such as anomaly detection or classification. The primary premise of the CS method is that most real-world signals have a compact representation in a transform domain where just a few are significant, while the remaining are zero or inconsequential this condition is known as signal sparsity. The advantage of learning by manipulating deep neural network [9] is that the network can do feature extraction without the need for a human to do it manually. Deep neural networks developed throughout time, with more profound network topologies delving deeper into hidden layers. The convolutional neural network (CNN) is one of the most well-known deep neural networks in pattern recognition [10]. Conventionally, CNN [11] has made significant contributions to image processing due to its remarkable capacity to generate meaningful feature maps [12] and information for conducting classification [13], object recognition [14], and signal analysis [15; 16; 17]. Deep convolution neural network is very promising in image classification problem in which the CNN model is trained with the training dataset that comprises of images belonging to different classes and the CNN model can classify a test image as a subset of one of the existing classes. In the classical classification problem, the complete uncompressed raw images are used in the training process and it is a challenge to classify a test image if it is in compressed form and the challenges are both in terms of validation accuracy and the computational time required to decompress the compressed image. One of the interesting solution is to perform the training and validation process in the compressed domain. In this approach the learning happens directly on the compressed information and validation also done in the same compressed domain. The compressed domain inference has been studied decade before [18; 19; 20; 21; 22; 23; 24; 25; 26] and many work have been extended recently [27; 28; 29; 30; 31; 32; 33]. The key benefits of this approach are two-fold: (i) There is no need for direct reconstruction computation process; and (ii) the amount of information to be communicated through networks to the server is also minimized. As discussed earlier, CS is a paradigm shift in the sampling and compression process in which only required number of measurements are done on the signal and the signal is reconstructed using optimization techniques [25; 26] and the method of learning on CS measurements will enjoy the compactness of the CS. [PERSON] et al. [22] used the matched filter of compressed sensing patterns applied to a library of pictures to produce a'smashed' filter and proved the validity of the random projection-based strategy for compressed domain image classification. For the same objective, [PERSON] et al. utilized the same SPC system but learnt sensing patterns using data-dependent \"secant projections\" [23]. The convolutional neural network (CNN) has recently been used [19; 20; 21; 22] and has generated substantially higher results. On the other hand, current neural network approaches, need to build a distinct network model for each individual measurement rate (MR). In this proposed work, we focus on leveraging neural networks to conduct image classification directly on CS measurements without reconstruction, as well as pushing the limits of object recognition and classification with very low sensing and processing resources.