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Found 12 papers, 3 papers with code
SGCNeRF: Few-Shot Neural Rendering via Sparse Geometric Consistency Guidance
Neural Radiance Field (NeRF) technology has made significant strides in creating novel viewpoints.
On the Dynamics Under the Unhinged Loss and Beyond
In this paper, we introduce the unhinged loss, a concise loss function, that offers more mathematical opportunities to analyze the closed-form dynamics while requiring as few simplifications or assumptions as possible.
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study
Large amounts of incremental learning algorithms have been proposed to alleviate the catastrophic forgetting issue arises while dealing with sequential data on a time series.
Super-Resolving Face Image by Facial Parsing Information
In this paper, we build a novel parsing map guided face super-resolution network which extracts the face prior (i. e., parsing map) directly from low-resolution face image for the following utilization.
Prototype-Anchored Learning for Learning with Imperfect Annotations
We verify the effectiveness of PAL on class-imbalanced learning and noise-tolerant learning by extensive experiments on synthetic and real-world datasets.
Learning Towards the Largest Margins
One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power.
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon
A new type of non-invasive attacks emerged recently, which attempt to cast perturbation onto the target by optics based tools, such as laser beam and projector.
Learning with Noisy Labels via Sparse Regularization
In this paper, we theoretically prove that \textbf{any loss can be made robust to noisy labels} by restricting the network output to the set of permutations over a fixed vector.
Fully Unsupervised Person Re-identification viaSelective Contrastive Learning
In this work, we propose a novel selective contrastive learning framework for unsupervised feature learning.
Single Image Deraining via Scale-space Invariant Attention Neural Network
Image enhancement from degradation of rainy artifacts plays a critical role in outdoor visual computing systems.
Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis
However, the performance of CNN-based classification approach depends on a large amount of high-quality manually labeled training data, which are inevitably introduced noise on labels in practice, leading to model overfitting and performance degradation.
ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising
Hyperspectral image (HSI) denoising is of crucial importance for many subsequent applications, such as HSI classification and interpretation.
