Deep Learning models designed for image classification have consistently achieved state-of-the-art performance across a plethora of applications. However, the presence of noisy samples, i.e., instances with incorrect labels, is a prevalent challenge in datasets derived from real-world applications. The training of Deep Learning models on such datasets inevitably compromises their performance. State-of-the-art models, such as Co-teaching+ and Jocor, utilize the Small Loss Approach (SLA) technique to handle noisy samples in a multi-class scenario. In this work, a new technique named Recovering Discarded Samples (RDS) was developed to address noisy samples, working with SLA. To demonstrate the effectiveness of the technique, RDS was applied to the Co-teaching+ and Jocor models, resulting in two new models, RDS-C and RDS-J. The results indicate gains of up to 6 percent in test metrics for both models. A third model, named RDS-Contrastive, was also developed, surpassing the state-of-the-art by up to 4 percent in test accuracy. Furthermore, this work extended the SLA technique to the multilabel scenario, leading to the development of the SLA Multilabel (SLAM) technique. With this technique, two additional models for the multilabel scenario with noisy samples were developed. The models proposed in this work for the multiclass scenario were applied in a real-world environmental solution, while the models developed for the multilabel scenario were implemented as a solution for a real problem in the oil and gas industry.