Search Results for author:
Found 24 papers, 11 papers with code
LSROM: Learning Self-Refined Organizing Map for Fast Imbalanced Streaming Data Clustering
That is, the imbalanced degree of clusters varies in different streaming data chunks, leading to corruption in either the accuracy or the efficiency of streaming data analysis based on existing clustering methods.
Improve Knowledge Distillation via Label Revision and Data Selection
In addition to the supervision of ground truth, the vanilla KD method regards the predictions of the teacher as soft labels to supervise the training of the student model.
Trustworthy Partial Label Learning with Out-of-distribution Detection
PLL-OOD significantly enhances model adaptability and accuracy by merging self-supervised learning with partial label loss and pioneering the Partial-Energy (PE) score for OOD detection.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+2
Federated Learning with Extremely Noisy Clients via Negative Distillation
The model trained on noisy labels serves as a `bad teacher' in knowledge distillation, aiming to decrease the risk of providing incorrect information.
Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge
Regarding the loss function, both intermediate and leaf nodes in the DAG graph are treated as target outputs during CINN training so as to drive co-learning of causal relationships among different types of nodes.
Feature Fusion from Head to Tail for Long-Tailed Visual Recognition
To rectify this issue, we propose to augment tail classes by grafting the diverse semantic information from head classes, referred to as head-to-tail fusion (H2T).
Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment
It is unfavorable for training on balanced data, but can be utilized to adjust the validity of the samples in long-tailed data, thereby solving the distorted embedding space of long-tailed problems.
Ranked #13 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition
Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead.
Feature-Balanced Loss for Long-Tailed Visual Recognition
Deep neural networks frequently suffer from performance degradation when the training data is long-tailed because several majority classes dominate the training, resulting in a biased model.
Ranked #14 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavation
To address this problem, state-of-the-art methods usually adopt a mixture of experts (MoE) to focus on different parts of the long-tailed distribution.
Federated Semi-Supervised Learning with Annotation Heterogeneity
Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data.
Uniform tensor clustering by jointly exploring sample affinities of various orders
Conventional clustering methods based on pairwise affinity usually suffer from the concentration effect while processing huge dimensional features yet low sample sizes data, resulting in inaccuracy to encode the sample proximity and suboptimal performance in clustering.
Label-Noise Learning with Intrinsically Long-Tailed Data
In this case, it is hard to distinguish clean samples from noisy samples on the intrinsic tail classes with the unknown intrinsic class distribution.
Compact Neural Networks via Stacking Designed Basic Units
To this end, this paper presents a new method termed TissueNet, which directly constructs compact neural networks with fewer weight parameters by independently stacking designed basic units, without requiring additional judgement criteria anymore.
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation
Federated learning provides a privacy guarantee for generating good deep learning models on distributed clients with different kinds of data.
Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data
Despite enormous research interest and rapid application of federated learning (FL) to various areas, existing studies mostly focus on supervised federated learning under the horizontally partitioned local dataset setting.
FDDH: Fast Discriminative Discrete Hashing for Large-Scale Cross-Modal Retrieval
To tackle these issues, we formulate the learning of similarity-preserving hash codes in terms of orthogonally rotating the semantic data so as to minimize the quantization loss of mapping such data to hamming space, and propose an efficient Fast Discriminative Discrete Hashing (FDDH) approach for large-scale cross-modal retrieval.
Concept drift-tolerant transfer learning in dynamic environments.
Then, a domain-wise weighted ensemble is introduced to combine the source and target models to select useful knowledge of each domain.
Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine
The main feature of the Dynamic Multi-objective Optimization Problems (DMOPs) is that optimization objective functions will change with times or environments.
Bayes Imbalance Impact Index: A Measure of Class Imbalanced Dataset for Classification Problem
To the best of our knowledge, there is no any measurement about the extent of influence of class imbalance on the classification performance of imbalanced data.
MTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval
More specifically, MTFH exploits an efficient objective function to flexibly learn the modality-specific hash codes with different length settings, while synchronously learning two semantic correlation matrices to semantically correlate the different hash representations for heterogeneous data comparable.
Homography Estimation From the Common Self-Polar Triangle of Separate Ellipses
In this paper, we address the problem of homography estimation from two separate ellipses.
Competitive and Penalized Clustering Auto-encoder
In this paper, we present a novel regularization method based on a clustering algorithm which is able to classify the parameters into different groups.
The Common Self-Polar Triangle of Concentric Circles and Its Application to Camera Calibration
In this paper, we explore the properties of the common self-polar triangle, when the two conics happen to be concentric circles.
