CVPR 2024 机器学习方向总汇（）
1、Machine Learning(机器学习)多任务、联邦学习、迁移学习和对抗等
- Molecular Data Programming: Towards Molecule Pseudo-labeling with Systematic Weak Supervision
👍摘要 - Improving Physics-Augmented Continuum Neural Radiance Field-Based Geometry-Agnostic System Identification with Lagrangian Particle Optimization
🏠project - Circuit Design and Efficient Simulation of Quantum Inner Product and Empirical Studies of Its Effect on Near-Term Hybrid Quantum-Classic Machine Learning
- 对抗
- Infrared Adversarial Car Stickers
- Robust Distillation via Untargeted and Targeted Intermediate Adversarial Samples
- Revisiting Adversarial Training Under Long-Tailed Distributions
- PAD: Patch-Agnostic Defense against Adversarial Patch Attacks
⭐code - Structured Gradient-based Interpretations via Norm-Regularized Adversarial Training
- MimicDiffusion: Purifying Adversarial Perturbation via Mimicking Clean Diffusion Model对抗性扰动
- Towards Transferable Targeted 3D Adversarial Attack in the Physical World
- Deep-TROJ: An Inference Stage Trojan Insertion Algorithm through Efficient Weight Replacement Attack攻击
- Attack To Defend: Exploiting Adversarial Attacks for Detecting Poisoned Models
- Re-thinking Data Availability Attacks Against Deep Neural Networks
- SlowFormer: Adversarial Attack on Compute and Energy Consumption of Efficient Vision Transformers
- Re-thinking Data Availablity Attacks Against Deep Neural Networks攻击
- NAPGuard: Towards Detecting Naturalistic Adversarial Patches
- Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training
- Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained Vision Transfomers后门攻击
- Physical Backdoor: Towards Temperature-based Backdoor Attacks in the Physical World
- Backdoor Defense via Test-Time Detecting and Repairing
- Nearest Is Not Dearest: Towards Practical Defense against Quantization-conditioned Backdoor Attacks
- Semantic-Aware Multi-Label Adversarial Attacks对抗攻击
- Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning
- Improving Transferable Targeted Adversarial Attacks with Model Self-Enhancement对抗攻击
- On the Robustness of Large Multimodal Models Against Image Adversarial Attacks
- Incremental Residual Concept Bottleneck Models
- Revisiting Adversarial Training at Scale
⭐code - Language-Driven Anchors for Zero-Shot Adversarial Robustness零样本对抗
- Transferable Structural Sparse Adversarial Attack Via Exact Group Sparsity Training
- Learning to Transform Dynamically for Better Adversarial Transferability
- Defense without Forgetting: Continual Adversarial Defense with Anisotropic & Isotropic Pseudo Replay
- Boosting Adversarial Transferability by Block Shuffle and Rotation
⭐code对抗性可转移性 - MMCert: Provable Defense against Adversarial Attacks to Multi-modal Models
- Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness
👍VILP - Adversaral Doodles: Interpretable and Human-drawable Attacks Provide Describable Insights
- PeerAiD: Improving Adversarial Distillation from a Specialized Peer Tutor
- Revisiting Adversarial Training under Long-Tailed Distributions
⭐code - Towards Fairness-Aware Adversarial Learning
- Dispel Darkness for Better Fusion: A Controllable Visual Enhancer based on Cross-modal Conditional Adversarial Learning
- Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement
- Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSM
- Boosting Adversarial Training via Fisher-Rao Norm-based Regularization
⭐code - A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning攻击
- 后门攻击
- 持续学习
- RCL: Reliable Continual Learning for Unified Failure Detection
- Consistent Prompting for Rehearsal-Free Continual Learning
- Improving Plasticity in Online Continual Learning via Collaborative Learning
- Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters
⭐code - Enhancing Visual Continual Learning with Language-Guided Supervision
- Convolutional Prompting meets Language Models for Continual Learning
- Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning
- Orchestrate Latent Expertise: Advancing Online Continual Learning with Multi-Level Supervision and Reverse Self-Distillation
- InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning
- Learning Equi-angular Representations for Online Continual Learning
- BrainWash: A Poisoning Attack to Forget in Continual Learning
- Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual Learning持续学习
- Traceable Federated Continual Learning
- Interactive Continual Learning: Fast and Slow Thinking
- 增量学习
- 类增量学习
- Dual-Consistency Model Inversion for Non-Exemplar Class Incremental Learning
- Class Incremental Learning with Multi-Teacher Distillation
- Dual-Enhanced Coreset Selection with Class-wise Collaboration for Online Blurry Class Incremental Learning
- Generative Multi-modal Models are Good Class Incremental Learners
- FCS: Feature Calibration and Separation for Non-Exemplar Class Incremental Learning
- OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental Learning
- Long-Tail Class Incremental Learning via Independent Sub-prototype Construction
- Gradient Reweighting: Towards Imbalanced Class-Incremental Learning
- DYSON: Dynamic Feature Space Self-Organization for Online Task-Free Class Incremental Learning
- NICE: Neurogenesis Inspired Contextual Encoding for Replay-free Class Incremental Learning
⭐code - Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning
⭐code - Text-Enhanced Data-free Approach for Federated Class-Incremental Learning
⭐code - Generative Multi-modal Models are Good Class-Incremental Learners
⭐code - Task-Adaptive Saliency Guidance for Exemplar-free Class Incremental Learning
⭐code
- 多任务
- Masked AutoDecoder is Effective Multi-Task Vision Generalist
- OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning
- Task-conditioned adaptation of visual features in multi-task policy learning
- DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data
⭐code - FedHCA2: Towards Hetero-Client Federated Multi-Task Learning
⭐code - MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning
- Joint-Task Regularization for Partially Labeled Multi-Task Learning
- Task-Conditioned Adaptation of Visual Features in Multi-Task Policy Learning
- 多标签学习
- 多视角学习
- 元学习
- 联邦学习
- An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
- Decentralized Directed Collaboration for Personalized Federated Learning
- Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data
- Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping
- FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning
- Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space
- Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning
- Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
- FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning
- Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity
⭐code - Global and Local Prompts Cooperation via Optimal Transport for Federated Learning
- PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees
⭐code - Relaxed Contrastive Learning for Federated Learning
- DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning
- FedAS: Bridging Inconsistency in Personalized Federated Learning
⭐code - Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning
- Data Valuation and Detections in Federated Learning
⭐code - An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning
⭐code - Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning
- FedUV: Uniformity and Variance for Heterogeneous Federated Learning
- FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
- Communication-Efficient Federated Learning with Accelerated Client Gradient
- 强化学习
- Improving Unsupervised Hierarchical Representation with Reinforcement Learning
- AlignSAM: Aligning Segment Anything Model to Open Context via Reinforcement Learning强化学习
- Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning
- POCE: Primal Policy Optimization with Conservative Estimation for Multi-constraint Offline Reinforcement Learning
- DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement Learning
- Learning to Control Camera Exposure via Reinforcement Learning
🏠project - Regularized Parameter Uncertainty for Improving Generalization in Reinforcement Learning
- Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World
🏠project
- 多模态机器学习
- 迁移学习
- Model Inversion Robustness: Can Transfer Learning Help?
- Enhanced Motion-Text Alignment for Image-to-Video Transfer Learning
- Structured Model Probing: Empowering Efficient Transfer Learning by Structured Regularization
- UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory
⭐code - Initialization Matters for Adversarial Transfer Learning
- 对比学习
- Improving Graph Contrastive Learning via Adaptive Positive Sampling
- MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning
- BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning
- Universal Novelty Detection Through Adaptive Contrastive Learning
- NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions in Diffusion Models
🏠project
- 模仿学习
- 上下文学习
- 弱监督学习
- 启示学习
原文地址：https://blog.csdn.net/qq_36812406/article/details/145188276
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