Self-Learning Deep Learning Model for Adaptive Image Recognition

The traditionally operated deep learning approach has a weakness in addressing novel and unseen data (Out-of-Distribution, or OOD) that leads to poor performance and necessitates human re-training. In order to solve this problem, the suggested system entails a modular, event triggered pipeline that recycles a number of significant phases. The first module is the OOD Detection Module, which detects new inputs. Then, when it is detected, it crawls the web to get the images which are visually or semantically similar. Then noise ought to be filtered off and pseudo-labeling of the raw data carried out to ensure quality. More to the point, an engine of high-quality and robust features of Self-Supervised Representation Learning is extracted on the foundation of filtered and weakly labeled web data through SimCLR or MoCo-v2. Finally, an Online Fine-Tuning and Continual Learning Framework exists. This will update the underlying classifier in a gradual way, through a replay buffer and mechanisms like Elastic Weight Consolidation (EWC). This will reduce catastrophic forgetting. This is a scalable and label-efficient approach to the continuous model evolution. This work preconditions further adaptive and continuous learning AI models in real-world scenarios by proactively growing its knowledge base. In such a way, it does not rely so heavily on manual annotations.