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Found 12 papers, 6 papers with code
RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation
Catastrophic forgetting of previous knowledge is a critical issue in continual learning typically handled through various regularization strategies.
Asynchronous Federated Continual Learning
We introduce a novel federated learning setting (AFCL) where the continual learning of multiple tasks happens at each client with different orderings and in asynchronous time slots.
Learning with Style: Continual Semantic Segmentation Across Tasks and Domains
Then, we address the proposed setup by using style transfer techniques to extend knowledge across domains when learning incremental tasks and a robust distillation framework to effectively recollect task knowledge under incremental domain shift.
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning
Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift in real-world Semantic Segmentation (SS) without compromising the private nature of the collected data.
Bring Evanescent Representations to Life in Lifelong Class Incremental Learning
In Class Incremental Learning (CIL), a classification model is progressively trained at each incremental step on an evolving dataset of new classes, while at the same time, it is required to preserve knowledge of all the classes observed so far.
RECALL: Replay-based Continual Learning in Semantic Segmentation
Replay data are then blended with new samples during the incremental steps.
Road Scenes Segmentation Across Different Domains by Disentangling Latent Representations
Deep learning models obtain impressive accuracy in road scenes understanding, however they need a large quantity of labeled samples for their training.
Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation
Deep convolutional neural networks for semantic segmentation achieve outstanding accuracy, however they also have a couple of major drawbacks: first, they do not generalize well to distributions slightly different from the one of the training data; second, they require a huge amount of labeled data for their optimization.
Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings
Deep learning frameworks allowed for a remarkable advancement in semantic segmentation, but the data hungry nature of convolutional networks has rapidly raised the demand for adaptation techniques able to transfer learned knowledge from label-abundant domains to unlabeled ones.
Unsupervised Domain Adaptation in Semantic Segmentation: a Review
The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation.
Unsupervised Domain Adaptation with Multiple Domain Discriminators and Adaptive Self-Training
We introduce a novel UDA framework where a standard supervised loss on labeled synthetic data is supported by an adversarial module and a self-training strategy aiming at aligning the two domain distributions.
Unsupervised Domain Adaptation for Mobile Semantic Segmentation based on Cycle Consistency and Feature Alignment
The supervised training of deep networks for semantic segmentation requires a huge amount of labeled real world data.
