… TrashNet, a commonly used waste … waste object, and this study found that the detection
model trained with a single object dataset was not only unsuitable for sorting multiple waste …

… As for the supervised part, the classification networks were trained on the classify-waste
benchmark. For the unsupervised part (pseudolabeling), unlabeled litter from the openlittermap …

… IDRL-RWODC) for the identification and classification of waste, especially for smart cities…
detecting objects followed by deep reinforcement learning referred to as the Q-learning network …

… and lower bounds of our learning rate where the network is actually learning, we trained our
… to trash classification. This paper focuses on object identification based on municipal waste …

… and probabilistic methods of the expectation-maximization type were conducted to
evaluate the potential of the classification approach based on the DBN (Deep Belief Network). …

… network is built, it tries to predict the output as close as possible to the actual value. The
accuracy of the network … process for classifying trash using the AlexNet deep learning method. …

… Deep learning-based methods used now do far better than … used or adapted deep learning
models for waste detection and … network model to perform waste detection and recognition. …

… network, and a group of joint learning multi-task subnets. To achieve joint optimization of
waste identification … -label waste classification task and the waste recognition and localization …

… deep convolutional neural network by combining several strategies to realize intelligent waste
recognition … The proposed network should be able to detect and classify different types of …

… YOLO uses an ANN (Artificial Neural Network) approach to detect objects in an image.
This network divides the image into several regions and predicts each bounding box and …