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Found 22 papers, 2 papers with code
A Sliding Layer Merging Method for Efficient Depth-Wise Pruning in LLMs
In particular, in the experiment with 35\% pruning on the Vicuna-7B model, our method achieved a 1. 654\% improvement in average performance on zero-shot tasks compared to the existing method.
An Efficient Framework for Enhancing Discriminative Models via Diffusion Techniques
Image classification serves as the cornerstone of computer vision, traditionally achieved through discriminative models based on deep neural networks.
DSDE: Using Proportion Estimation to Improve Model Selection for Out-of-Distribution Detection
In this paper, we emphasize the significance of the proportion of models in the library that identify the test sample as an OoD sample.
AdvLogo: Adversarial Patch Attack against Object Detectors based on Diffusion Models
With the rapid development of deep learning, object detectors have demonstrated impressive performance; however, vulnerabilities still exist in certain scenarios.
Technique Report of CVPR 2024 PBDL Challenges
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
Provable Contrastive Continual Learning
In this work, we fill this gap by establishing theoretical performance guarantees, which reveal how the performance of the model is bounded by training losses of previous tasks in the contrastive continual learning framework.
Enhancing Out-of-Distribution Detection with Multitesting-based Layer-wise Feature Fusion
When trained using KNN on CIFAR10, MLOD-Fisher significantly lowers the false positive rate (FPR) from 24. 09% to 7. 47% on average compared to merely utilizing the features of the last layer.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Enhancing the Power of OOD Detection via Sample-Aware Model Selection
In this work we present a novel perspective on detecting out-of-distribution (OOD) samples and propose an algorithm for sample-aware model selection to enhance the effectiveness of OOD detection.
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.
Boosting Out-of-Distribution Detection with Multiple Pre-trained Models
This advance raises a natural question: Can we leverage the diversity of multiple pre-trained models to improve the performance of post hoc detection methods?
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\%.
Boosting Out-of-distribution Detection with Typical Features
Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios.
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL
In this study, we quantify the agent's behavior difference and build its relationship with the policy performance via {\bf Role Diversity}, a metric to measure the characteristics of MARL tasks.
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.
NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models
Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks.
Towards a Theoretical Framework of Out-of-Distribution Generalization
We also introduce a new concept of expansion function, which characterizes to what extent the variance is amplified in the test domains over the training domains, and therefore give a quantitative meaning of invariant features.
Out-of-Distribution Generalization Analysis via Influence Function
One of the definitions of OOD accuracy is worst-domain accuracy.
MetaAugment: Sample-Aware Data Augmentation Policy Learning
Automated data augmentation has shown superior performance in image recognition.
Provable More Data Hurt in High Dimensional Least Squares Estimator
Furthermore, the finite-sample distribution and the confidence interval of the prediction risk are provided.
Risk Variance Penalization
The key of the out-of-distribution (OOD) generalization is to generalize invariance from training domains to target domains.
