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09/01/2023
Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering
Domain adaptation (DA) aims to alleviate the domain shift between source...
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08/15/2023
Ternary Singular Value Decomposition as a Better Parameterized Form in Linear Mapping
We present a simple yet novel parameterized form of linear mapping to ac...
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03/05/2023
Reparameterization through Spatial Gradient Scaling
Reparameterization aims to improve the generalization of deep neural net...
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02/21/2023
A General-Purpose Transferable Predictor for Neural Architecture Search
Understanding and modelling the performance of neural architectures is k...
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11/30/2022
AIO-P: Expanding Neural Performance Predictors Beyond Image Classification
Evaluating neural network performance is critical to deep neural network...
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11/30/2022
GENNAPE: Towards Generalized Neural Architecture Performance Estimators
Predicting neural architecture performance is a challenging task and is ...
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10/04/2022
Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification
Lifelong object re-identification incrementally learns from a stream of ...
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06/07/2022
One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift
In this paper, we investigate open-set recognition with domain shift, wh...
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05/13/2022
R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning
Systematicity, i.e., the ability to recombine known parts and rules to f...
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05/09/2022
Local Prediction Aggregation: A Frustratingly Easy Source-free Domain Adaptation Method
We propose a simple but effective source-free domain adaptation (SFDA) m...
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11/09/2021
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition
Most meta-learning approaches assume the existence of a very large set o...
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10/17/2021
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization
Anderson mixing has been heuristically applied to reinforcement learning...
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10/08/2021
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation
Domain adaptation (DA) aims to alleviate the domain shift between source...
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09/25/2021
Profiling Neural Blocks and Design Spaces for Mobile Neural Architecture Search
Neural architecture search automates neural network design and has achie...
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09/25/2021
L^2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning
Neural architecture search (NAS) has achieved remarkable results in deep...
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09/17/2021
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations
In real scenarios, state observations that an agent observes may contain...
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08/03/2021
Generalized Source-free Domain Adaptation
Domain adaptation (DA) aims to transfer the knowledge learned from a sou...
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05/19/2021
Generative Adversarial Neural Architecture Search
Despite the empirical success of neural architecture search (NAS) in dee...
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04/28/2021
MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains
GANs largely increases the potential impact of generative models. Theref...
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10/23/2020
Unsupervised Domain Adaptation without Source Data by Casting a BAIT
Unsupervised domain adaptation (UDA) aims to transfer the knowledge lear...
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09/01/2020
Neural Architecture Search For Keyword Spotting
Deep neural networks have recently become a popular solution to keyword ...
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04/20/2020
Generative Feature Replay For Class-Incremental Learning
Humans are capable of learning new tasks without forgetting previous one...
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04/01/2020
Semantic Drift Compensation for Class-Incremental Learning
Class-incremental learning of deep networks sequentially increases the n...
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09/30/2019
RNAS: Architecture Ranking for Powerful Networks
Neural Architecture Search (NAS) is attractive for automatically produci...
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09/30/2019