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Found 28 papers, 9 papers with code
Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning
Self-supervised learning (SSL) has emerged as an effective paradigm for deriving general representations from vast amounts of unlabeled data.
Towards Non-Exemplar Semi-Supervised Class-Incremental Learning
On the other hand, Semi-IPC learns a prototype for each class with unsupervised regularization, enabling the model to incrementally learn from partially labeled new data while maintaining the knowledge of old classes.
Multi-scale Unified Network for Image Classification
Convolutional Neural Networks (CNNs) have advanced significantly in visual representation learning and recognition.
Continual Forgetting for Pre-trained Vision Models
(i) For unwanted knowledge, efficient and effective deleting is crucial.
Active Generalized Category Discovery
Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task, which endeavors to cluster unlabeled samples from both novel and old classes, leveraging some labeled data of old classes.
Revisiting Confidence Estimation: Towards Reliable Failure Prediction
Reliable confidence estimation is a challenging yet fundamental requirement in many risk-sensitive applications.
Open-world Machine Learning: A Review and New Outlooks
This paper aims to provide a comprehensive introduction to the emerging open-world machine learning paradigm, to help researchers build more powerful AI systems in their respective fields, and to promote the development of artificial general intelligence.
Federated Class-Incremental Learning with Prototype Guided Transformer
On the other hand, our approach utilizes a pre-trained model as the backbone and utilizes LoRA to fine-tune with a tiny amount of parameters when learning new classes.
Class Incremental Learning with Self-Supervised Pre-Training and Prototype Learning
This is hard for DNN because it tends to focus on fitting to new classes while ignoring old classes, a phenomenon known as catastrophic forgetting.
Towards Trustworthy Dataset Distillation
By distilling both InD samples and outliers, the condensed datasets are capable to train models competent in both InD classification and OOD detection.
OpenMix: Exploring Outlier Samples for Misclassification Detection
Reliable confidence estimation for deep neural classifiers is a challenging yet fundamental requirement in high-stakes applications.
Dynamics-Aware Loss for Learning with Label Noise
Label noise poses a serious threat to deep neural networks (DNNs).
Nonlinear Hyperspectral Unmixing based on Multilinear Mixing Model using Convolutional Autoencoders
Current deep learning-based nonlinear unmixing focuses on the models in additive, bilinear-based formulations.
Rethinking Confidence Calibration for Failure Prediction
We investigate this problem and reveal that popular confidence calibration methods often lead to worse confidence separation between correct and incorrect samples, making it more difficult to decide whether to trust a prediction or not.
Average of Pruning: Improving Performance and Stability of Out-of-Distribution Detection
Detecting Out-of-distribution (OOD) inputs have been a critical issue for neural networks in the open world.
Dual Alignment Unsupervised Domain Adaptation for Video-Text Retrieval
To tackle this, we propose a novel method named Dual Alignment Domain Adaptation (DADA).
Robust Multi-Read Reconstruction from Contaminated Clusters Using Deep Neural Network for DNA Storage
DNA has immense potential as an emerging data storage medium.
Class-Incremental Learning via Dual Augmentation
Deep learning systems typically suffer from catastrophic forgetting of past knowledge when acquiring new skills continually.
Delving into Feature Space: Improving Adversarial Robustness by Feature Spectral Regularization
Comprehensive experiments demonstrate that FSR is effective to alleviate the dominance of larger eigenvalues and improve adversarial robustness on different datasets.
Prototype Augmentation and Self-Supervision for Incremental Learning
Despite the impressive performance in many individual tasks, deep neural networks suffer from catastrophic forgetting when learning new tasks incrementally.
Misclassification Detection via Class Augmentation
In spite of the simplicity, extensive experiments demonstrate that the misclassification detection performance of DNNs can be significantly improved by seeing more generated pseudo-classes during training.
Improving Deep Hyperspectral Image Classification Performance with Spectral Unmixing
Recent advances in neural networks have made great progress in the hyperspectral image (HSI) classification.
Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field
To improve the classification performance in the context of hyperspectral image processing, many works have been developed based on two common strategies, namely the spatial-spectral information integration and the utilization of neural networks.
Ranked #8 on
Hyperspectral Image Classification
on Indian Pines
(Overall Accuracy metric)
General Classification
Hyperspectral Image Classification
+3
A CNN-based Spatial Feature Fusion Algorithm for Hyperspectral Imagery Classification
At the testing stage, by applying the discriminant model to the pixel-pairs generated by the test pixel and its neighbors, the local structure is estimated and represented as a customized convolutional kernel.
Spectral-Spatial Feature Extraction and Classification by ANN Supervised with Center Loss in Hyperspectral Imagery
In this paper, we propose a spectral-spatial feature extraction and classification framework based on artificial neuron network (ANN) in the context of hyperspectral imagery.
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixing problem.
Bi-Objective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models
Nonnegative matrix factorization (NMF) is a powerful class of feature extraction techniques that has been successfully applied in many fields, namely in signal and image processing.
Kernel Nonnegative Matrix Factorization Without the Curse of the Pre-image - Application to Unmixing Hyperspectral Images
The nonnegative matrix factorization (NMF) is widely used in signal and image processing, including bio-informatics, blind source separation and hyperspectral image analysis in remote sensing.
