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- Deep Domain Generalization via Conditional Invariant Adversarial Networks848
This work proposes an end-to-end conditional invariant deep domain generalization approach by leveraging deep neural networks for domain-invariant representation learning and proves the effectiveness of the proposed method.
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- CRIS: CLIP-Driven Referring Image Segmentation567
This paper designs a vision-language decoder to propagate fine-grained semantic information from textual representations to each pixel-level activation, which promotes consistency between the two modalities and presents text-to-pixel contrastive learning.
- 483
- DeepInception: Hypnotize Large Language Model to Be Jailbreaker438
A lightweight method to take advantage of the LLMs' personification capabilities to construct $\textit{a virtual, nested scene}$, allowing it to realize an adaptive way to escape the usage control in a normal scenario.
- Why ResNet Works? Residuals Generalize410
According to the obtained generalization bound, regularization terms should be introduced to control the magnitude of the norms of weight matrices not to increase too much, in practice, to ensure a good generalization ability, which justifies the technique of weight decay.
- Domain Adaptation with Conditional Transferable Components.374
This paper aims to extract conditional transferable components whose conditional distribution P(T(X)∣Y) is invariant after proper location-scale (LS) transformations, and identifies how P(Y) changes between domains simultaneously.
- Expressive power of parametrized quantum circuits337
It is proved that PQCs with a simple structure already outperform any classical neural network for generative tasks, unless the polynomial hierarchy collapses, and they are employed as an application for Bayesian learning.
- Domain Generalization via Entropy Regularization327
This work proposes an entropy regularization term that measures the dependency between the learned features and the class labels that is guaranteed to learn conditional-invariant features across all source domains and thus can learn classifiers with better generalization capabilities.
- Domain Generalization via Conditional Invariant Representations312
This paper proposes to learn a feature representation which has domain-invariant class conditional distributions P(h(X)|Y), and proposes a conditional invariant representation which can be guaranteed if the class prior P(Y) does not change across training and test domains.
- Multiple Kernel k-means with Incomplete Kernels306
Two simple yet effective algorithms that perform multiple kernel clustering directly with the presence of incomplete kernel matrices, which are treated as auxiliary variables to be jointly optimized and which consistently achieve superior performance are proposed.
- Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces305
The intra-class constraint of ArcFace is relaxed to improve the robustness to label noise and recapture intra-compactness, decrease the influence from noise, and achieve comparable performance compared to ArcFace trained on the manually cleaned dataset.
- Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning298
This paper introduces an intermediate class to avoid directly estimating the noisy class posterior of the transition matrix, and introduces the dual $T-estimator for estimating transition matrices, leading to better classification performances.
- Fast Supervised Discrete Hashing283
This paper proposes a new learning-based hashing method called "fast supervised discrete hashing" (FSDH) based on “supervised discrete hashing” (SDH), which uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm.
- Selective-Supervised Contrastive Learning with Noisy Labels258
Experiments on multiple noisy datasets demonstrate the robustness of the learned representations by the proposed selective-supervised contrastive learning (Sel-CL), following the state-of-the-art performance.
- Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence247
A PAC-Bayes generalization bound for neural networks trained by SGD is proved, which has a positive correlation with the ratio of batch size to learning rate, which builds the theoretical foundation of the training strategy.
- Learning with Biased Complementary Labels224
A framework that provides a general method to modify traditional loss functions and extends standard deep neural network classifiers to learn with biased complementary labels and theoretically ensures that the classifier learned with complementary labels converges to the optimal one learned with true labels.
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- Quantum noise protects quantum classifiers against adversaries182
By taking advantage of depolarisation noise in quantum circuits for classification, a robustness bound against adversaries can be derived where the robustness improves with increasing noise, which is the first quantum protocol that can be used against the most general adversaries.
- Semantic Structure-based Unsupervised Deep Hashing176
This work designs a deep architecture and a pair-wise loss function to preserve the semantic structure of the semantic relationships between points in unsupervised settings, and shows that SSDH significantly outperforms current state-of-the-art methods.
- 173
- Orthogonal Deep Neural Networks169
This paper proves that DNNs are of local isometry on data distributions of practical interest, and establishes a new generalization error bound that is both scale- and range-sensitive to singular value spectrum of each of networks’ weight matrices.
- DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs169
A new deep unsupervised hashing model, called DistilHash, is proposed, which can learn a distilled data set, where data pairs have confident similarity signals and the semantic similarity labels assigned by the optimal Bayesian classifier can be potentially distilled.
- FedFed: Feature Distillation against Data Heterogeneity in Federated Learning167
This work proposes a novel approach called FedFed, which partitions data into performance-sensitive features and performance-robust features that are globally shared to mitigate data heterogeneity, while the performance-Robust features are kept locally.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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