| _ | unlearning | _ |
|---|
| Boundary | unlearning | : Rapid Forgetting of Deep Networks via Shifting the Decision Boundary |
| Challenging Forgets: Unveiling the Worst-case Forget Sets in Machine | unlearning | |
| converging | unlearning | algorithm for the Hopfield neural network: optimal strategy, The |
| Decoupled Distillation to Erase: A General | unlearning | Method for Any Class-centric Tasks |
| Deep | unlearning | via Randomized Conditionally Independent Hessians |
| Disposable Transfer Learning for Selective Source Task | unlearning | |
| Efficient Two-stage Model Retraining for Machine | unlearning | |
| ERM-KTP: Knowledge-Level Machine | unlearning | via Knowledge Transfer |
| Fast-NTK: Parameter-Efficient | unlearning | for Large-Scale Models |
| FixGuard: Repairing Backdoored Models via Class-Wise Trigger Recovery and | unlearning | |
| Forget More to Learn More: Domain-specific Feature | unlearning | for Semi-supervised and Unsupervised Domain Adaptation |
| Generative | unlearning | for Any Identity |
| Identify Backdoored Model in Federated Learning via Individual | unlearning | |
| Illusion of | unlearning | : The Unstable Nature of Machine Unlearning in Text-to-Image Diffusion Models, The |
| Illusion of | unlearning | : The Unstable Nature of Machine Unlearning in Text-to-Image Diffusion Models, The |
| Is Retain Set All You Need in Machine | unlearning | ? Restoring Performance of Unlearned Models with Out-of-distribution Images |
| Learn to Unlearn for Deep Neural Networks: Minimizing | unlearning | Interference with Gradient Projection |
| Learn to Unlearn: Insights Into Machine | unlearning | |
| Learning to Unlearn for Robust Machine | unlearning | |
| LoTUS: Large-Scale Machine | unlearning | with a Taste of Uncertainty |
| Machine | unlearning | in Hyperbolic vs. Euclidean Multimodal Contrastive Learning: Adapting Alignment Calibration to MERU |
| Multidelete for Multimodal Machine | unlearning | |
| MUter: Machine | unlearning | on Adversarially Trained Models |
| NoT: Federated | unlearning | via Weight Negation |
| Preserving privacy without compromising accuracy: Machine | unlearning | for handwritten text recognition |
| Revisiting Machine | unlearning | with Dimensional Alignment |
| SAFE: Machine | unlearning | With Shard Graphs |
| Study Regarding Machine | unlearning | on Facial Attribute Data, A |
| Towards Efficient Machine | unlearning | with Data Augmentation: Guided Loss-Increasing (GLI) to Prevent the Catastrophic Model Utility Drop |
| Towards Natural Machine | unlearning | |
| Towards Source-Free Machine | unlearning | |
| unlearning | through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter |
| unlearning | through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter |
| Zero-Shot Class | unlearning | in CLIP with Synthetic Samples |
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| _ | unleashing | _ |
|---|
| Adaptive Dropout: | unleashing | Dropout across Layers for Generalizable Image Super-Resolution |
| BiKT: | unleashing | the Potential of GNNs via Bi-Directional Knowledge Transfer |
| CFAT: | unleashing | Triangular Windows for Image Super-resolution |
| ChangeViT: | unleashing | plain vision transformers for change detection in remote sensing images |
| COMPGS: | unleashing | 2D Compositionality for Compositional Text-to-3D via Dynamically Optimizing 3D Gaussians |
| Depth Anything: | unleashing | the Power of Large-Scale Unlabeled Data |
| DiffHSR: | unleashing | Diffusion Priors in Hyperspectral Image Super-Resolution |
| Diffloss: | unleashing | Diffusion Model as Constraint for Training Image Restoration Network |
| DiffMorpher: | unleashing | the Capability of Diffusion Models for Image Morphing |
| EasyHOI: | unleashing | the Power of Large Models for Reconstructing Hand-Object Interactions in the Wild |
| Empowering Object Detection: | unleashing | the Potential of Decoupled and Interactive Distillation |
| FaithDiff: | unleashing | Diffusion Priors for Faithful Image Super-resolution |
| Geowizard: | unleashing | the Diffusion Priors for 3d Geometry Estimation from a Single Image |
| GPT4Ego: | unleashing | the Potential of Pre-Trained Models for Zero-Shot Egocentric Action Recognition |
| HAFormer: | unleashing | the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation |
| HoVLE: | unleashing | the Power of Monolithic Vision-Language Models with Holistic Vision-Language Embedding |
| l8-Robustness and Beyond: | unleashing | Efficient Adversarial Training |
| MAP: | unleashing | Hybrid Mamba-Transformer Vision Backbone's Potential with Masked Autoregressive Pretraining |
| NAVGPT-2: | unleashing | Navigational Reasoning Capability for Large Vision-language Models |
| NTRENet++: | unleashing | the Power of Non-Target Knowledge for Few-Shot Semantic Segmentation |
| ReferSAM: | unleashing | Segment Anything Model for Referring Image Segmentation |
| SC- Tune: | unleashing | Self-Consistent Referential Comprehension in Large Vision Language Models |
| SD-DiT: | unleashing | the Power of Self-Supervised Discrimination in Diffusion Transformer* |
| SEGIC: | unleashing | the Emergent Correspondence for In-context Segmentation |
| Textdiffuser-2: | unleashing | the Power of Language Models for Text Rendering |
| unleashing | Channel Potential: Space-Frequency Selection Convolution for SAR Object Detection |
| unleashing | Fine-Coarse Curve Perception Via Trunk-Branch Perturbation |
| unleashing | In-context Learning of Autoregressive Models for Few-shot Image Manipulation |
| unleashing | Knowledge Potential of Source Hypothesis for Source-Free Domain Adaptation |
| unleashing | Network Potentials for Semantic Scene Completion |
| unleashing | Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification |
| unleashing | Potentials of Vision-Language Models for Zero-Shot HOI Detection |
| unleashing | Text-to-Image Diffusion Models for Visual Perception |
| unleashing | Text-to-image Diffusion Prior for Zero-shot Image Captioning |
| unleashing | the Feature Hierarchy Potential: An Efficient Tri-Hybrid Person Search Model |
| unleashing | the Potential of Adaptation Models via Go-getting Domain Labels |
| unleashing | the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation |
| unleashing | the Potential of Hierarchical Region Clues for Open-Vocabulary Multi-Label Classification |
| unleashing | the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation |
| unleashing | the Potential of SAM for Medical Adaptation via Hierarchical Decoding |
| unleashing | the Potential of Spiking Neural Networks with Dynamic Confidence |
| unleashing | the Potential of the Semantic Latent Space in Diffusion Models for Image Dehazing |
| unleashing | the Power of Connected and Automated Vehicles: A Dedicated Link Strategy for Efficient Management of Mixed Traffic |
| unleashing | the Power of Each Distilled Image |
| unleashing | the Power of Generalized Iterative Closest Point for Swift and Effective Point Cloud Registration |
| unleashing | the Power of Gradient Signal-to-Noise Ratio for Zero-Shot NAS |
| unleashing | the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation |
| unleashing | the Power of Prompt-driven Nucleus Instance Segmentation |
| unleashing | Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes |
| unleashing | Unlabeled Data: A Paradigm for Cross-View Geo-Localization |
| unleashing | Vanilla Vision Transformer with Masked Image Modeling for Object Detection |
| Unveiling the Unknown: | unleashing | the Power of Unknown to Known in Open-Set Source-Free Domain Adaptation |
| UpGen: | unleashing | Potential of Foundation Models for Training-Free Camouflage Detection via Generative Models |
| VP3D: | unleashing | 2D Visual Prompt for Text-to-3D Generation |
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