Search Results for author:
Found 13 papers, 1 papers with code
ReLU Neural Networks, Polyhedral Decompositions, and Persistent Homolog
A ReLU neural network leads to a finite polyhedral decomposition of input space and a corresponding finite dual graph.
Hamming Similarity and Graph Laplacians for Class Partitioning and Adversarial Image Detection
Here, we investigate the potential for ReLU activation patterns (encoded as bit vectors) to aid in understanding and interpreting the behavior of neural networks.
Hierarchical Prompt Learning for Multi-Task Learning
Considering this, we present Hierarchical Prompt (HiPro) learning, a simple and effective method for jointly adapting a pre-trained VLM to multiple downstream tasks.
Dual Graphs of Polyhedral Decompositions for the Detection of Adversarial Attacks
This paper illustrates how one can utilize the dual graph to detect and analyze adversarial attacks in the context of digital images.
Self-Supervision Can Be a Good Few-Shot Learner
Specifically, we maximize the mutual information (MI) of instances and their representations with a low-bias MI estimator to perform self-supervised pre-training.
cross-domain few-shot learning
Unsupervised Few-Shot Image Classification
+1
Source-Free Domain Adaptation for Real-world Image Dehazing
Although certain Domain Adaptation (DA) dehazing methods have been presented, they inevitably require access to the source dataset to reduce the gap between the source synthetic and target real domains.
Prompt Distribution Learning
We present prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks.
Improving Fraud Detection via Hierarchical Attention-based Graph Neural Network
In this paper, we propose a Hierarchical Attention-based Graph Neural Network (HA-GNN) for fraud detection, which incorporates weighted adjacency matrices across different relations against camouflage.
Exposure Normalization and Compensation for Multiple-Exposure Correction
However, the procedures of correcting underexposure and overexposure to normal exposures are much different from each other, leading to large discrepancies for the network in correcting multiple exposures, thus resulting in poor performance.
Phoneme-based Distribution Regularization for Speech Enhancement
Specifically, we propose a phoneme-based distribution regularization (PbDr) for speech enhancement, which incorporates frame-wise phoneme information into speech enhancement network in a conditional manner.
Model-Free State Estimation Using Low-Rank Canonical Polyadic Decomposition
As electric grids experience high penetration levels of renewable generation, fundamental changes are required to address real-time situational awareness.
Compact Feature Learning for Multi-Domain Image Classification
However, they view the distributions of features from different classes as a general distribution and try to match these distributions across domains, which lead to the mixture of features from different classes across domains and degrade the performance of classification.
Deep Domain Generalization via Conditional Invariant Adversarial Networks
Under the assumption that the conditional distribution $P(Y|X)$ remains unchanged across domains, earlier approaches to domain generalization learned the invariant representation $T(X)$ by minimizing the discrepancy of the marginal distribution $P(T(X))$.
Ranked #67 on
Domain Generalization
on PACS
