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Self-Training for Class-Incremental Semantic Segmentation
We study incremental learning for semantic segmentation where when learn...
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Learning to Rank for Active Learning: A Listwise Approach
Active learning emerged as an alternative to alleviate the effort to lab...
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Generative Feature Replay For Class-Incremental Learning
Humans are capable of learning new tasks without forgetting previous one...
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Semantic Drift Compensation for Class-Incremental Learning
Class-incremental learning of deep networks sequentially increases the n...
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Continual Universal Object Detection
Object detection has improved significantly in recent years on multiple ...
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Learning Metrics from Teachers: Compact Networks for Image Embedding
Metric learning networks are used to compute image embeddings, which are...
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Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank
For many applications the collection of labeled data is expensive labori...
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Memory Replay GANs: learning to generate images from new categories without forgetting
Previous works on sequential learning address the problem of forgetting ...
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Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
We propose a novel crowd counting approach that leverages abundantly ava...
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Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting
In this paper we propose an approach to avoiding catastrophic forgetting...
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RankIQA: Learning from Rankings for No-reference Image Quality Assessment
We propose a no-reference image quality assessment (NR-IQA) approach tha...
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