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10/10/2022
Visual Prompt Tuning for Test-time Domain Adaptation
Models should have the ability to adapt to unseen data during test-time ...
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06/23/2022
Prototype-Anchored Learning for Learning with Imperfect Annotations
The success of deep neural networks greatly relies on the availability o...
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06/23/2022
Learning Towards the Largest Margins
One of the main challenges for feature representation in deep learning-b...
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05/25/2022
ReSmooth: Detecting and Utilizing OOD Samples when Training with Data Augmentation
Data augmentation (DA) is a widely used technique for enhancing the trai...
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07/31/2021
Learning with Noisy Labels via Sparse Regularization
Learning with noisy labels is an important and challenging task for trai...
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06/06/2021
Asymmetric Loss Functions for Learning with Noisy Labels
Robust loss functions are essential for training deep neural networks wi...
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03/30/2021
Exploiting Invariance in Training Deep Neural Networks
Inspired by two basic mechanisms in animal visual systems, we introduce ...
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07/16/2020
Advances in Deep Learning for Hyperspectral Image Analysis–Addressing Challenges Arising in Practical Imaging Scenarios
Deep neural networks have proven to be very effective for computer visio...
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04/30/2020
Out-of-the-box channel pruned networks
In the last decade convolutional neural networks have become gargantuan....
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10/25/2019
FineText: Text Classification via Attention-based Language Model Fine-tuning
Training deep neural networks from scratch on natural language processin...
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05/29/2019
