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Found 20 papers, 6 papers with code
Open-Vocabulary Object Detection with Meta Prompt Representation and Instance Contrastive Optimization
Without using knowledge distillation, ensemble model or extra training data during detector training, our proposed MIC outperforms previous SOTA methods trained with these complex techniques on LVIS.
Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization
The proliferation of pretrained models, as a result of advancements in pretraining techniques, has led to the emergence of a vast zoo of publicly available models.
Heavy-Tailed Regularization of Weight Matrices in Deep Neural Networks
To capitalize on this discovery, we introduce a novel regularization technique, termed Heavy-Tailed Regularization, which explicitly promotes a more heavy-tailed spectrum in the weight matrix through regularization.
Fair-CDA: Continuous and Directional Augmentation for Group Fairness
By adjusting the perturbation strength in the direction of the paths, our proposed augmentation is controllable and auditable.
ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization
We evaluate our paradigm on a diverse model zoo consisting of 35 models for various OoD tasks and demonstrate: (i) model ranking is better correlated with fine-tuning ranking than previous methods and up to 9859x faster than brute-force fine-tuning; (ii) OoD generalization after model ensemble with feature selection outperforms the state-of-the-art methods and the accuracy on most challenging task DomainNet is improved from 46. 5\% to 50. 6\%.
Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding
Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data.
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
First, we theoretically show the transferability of robustness from an adversarially trained teacher model to a student model with the help of mixup augmentation.
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture
In view of these, we propose DHA, which achieves joint optimization of Data augmentation policy, Hyper-parameter and Architecture.
NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization
In this work, we propose robust Neural Architecture Search for OoD generalization (NAS-OoD), which optimizes the architecture with respect to its performance on generated OoD data by gradient descent.
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Adversarial Robustness for Unsupervised Domain Adaptation
Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled target domain with deep models.
Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA), which aims to learn models in a partially labeled target domain with the assistance of the fully labeled source domain, attracts increasing attention in recent years.
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning
Our method performs co-optimization of the neural architectures, training hyper-parameters and data augmentation policies in an end-to-end fashion without the need of model retraining.
MetaAugment: Sample-Aware Data Augmentation Policy Learning
Automated data augmentation has shown superior performance in image recognition.
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation
To address that, we propose DecAug, a novel decomposed feature representation and semantic augmentation approach for OoD generalization.
VEGA: Towards an End-to-End Configurable AutoML Pipeline
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models.
Multi-objective Neural Architecture Search via Non-stationary Policy Gradient
Despite recent progress, the problem of approximating the full Pareto front accurately and efficiently remains challenging.
Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem
In this work, we propose a new approach that affords fast adaptation to previously unseen cameras, and robustness to changes in capture device by leveraging annotated samples across different cameras and datasets.
Deep Meta-Learning: Learning to Learn in the Concept Space
Few-shot learning remains challenging for meta-learning that learns a learning algorithm (meta-learner) from many related tasks.
Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial.
