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06/26/2023
DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing
Precise and controllable image editing is a challenging task that has at...
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05/29/2023
Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models
Public large-scale text-to-image diffusion models, such as Stable Diffus...
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04/02/2023
Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) aims at learning to classify...
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10/01/2022
Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning
Federated learning aims to train models collaboratively across different...
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12/09/2021
Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning
Class Incremental Learning (CIL) aims at learning a multi-class classifi...
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05/10/2021
Continual Learning via Bit-Level Information Preserving
Continual learning tackles the setting of learning different tasks seque...
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01/28/2021
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Transformers, which are popular for language modeling, have been explore...
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06/06/2019
Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric
The vulnerability to slight input perturbations is a worrying yet intrig...
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05/15/2019
Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks
In this work, we propose a novel technique to boost training efficiency ...
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05/19/2018