## 1 Introduction  
Information transmission through multimode fibers (MMFs) has been a relatively dormant field of research and development for many decades. More recently, MMFs are becoming widely used in telecommunications because of their high information capacity at a lower manufacturing cost [1]. The number of available channels in MMFs is limited by the number of supported spatial modes. The spatial modes are the solutions to the wave equation for a MMF and thus propagate unchanged along the fiber, offering unique mode multiplexing opportunities [2; 3; 4; 5]. MMFs have also attracted significant attention in endoscopy, for which a high information capacity is essential to achieve high quality images [6; 7; 8; 9; 10; 11]. However, the differences in propagation constants between the spatial fiber modes can hinder the information transmission through a MMF system. When an input image is coupled in a MMF, it distributes among the supported spatial modes, resulting in a speckle pattern at the fiber output.  
Several methods have been employed for reversing the scrambling caused by MMFs. Phase conjugation or measurement of the transmission matrix [7; 8; 11; 12] allow to reverse the effects of propagation and unscramble the information. While phase conjugation and transmission matrix can control the light propagation through an MMF probe, they are based on interferometric measurements of the light field using digital holography, and as a result they are susceptible to environmental or experimental perturbations, requiring dynamic recalibration [8; 13; 14; 15]. To overcome the sensitivity of the calibration-based measurements, deep neural networks (DNNs) have been proposed as an alternative for imaging through MMFs [16; 17; 18; 19; 20]. The idea of using artificial neural networks (ANNs)to interpret the information after propagation in a MMF was first reported by [PERSON] et al. [21; 22] in 1991 using a simple neural network architecture. Recently, the advancements in computational means allow the realization of more complex neural network architectures that can handle data of high-dimensionality, bringing DNNs to the forefront of many leading technologies ranging from research and business to military and entertainment [23; 24; 25]. DNNs have shown remarkable capabilities in recovering information through MMFs for imaging applications, while preserving robustness against perturbations in the system [16; 17; 18; 20; 26].  
The noise induced at the output speckle patterns due to perturbations caused by thermal, mechanical or source wavelength drift can be catastrophic for a calibration-based technique such as the transmission matrix but they affect, at a lower level, the performance of DNNs [16]. In addition, in cases that multiple wavelengths are needed for imaging, the transmission matrix of the fiber needs to be measured multiple times for different wavelengths [27]. Therefore, it would be time-efficient to investigate a way to remove recalibration of the system when the wavelength is changed. In the work presented in this article, we propose an alternative approach for recovering the information through the MMF system in the presence of a wavelength drift in the light source using DNNs. We investigate the performance of DNNs to classify the input images in a MMF from their corresponding speckle patterns in various wavelength drift cases. Remarkable results were obtained for wavelength drift bandwidth up tp 100 nm, and they are further discussed in the following paragraphs.