information loss
The reduction in information richness or quality that can occur during processes such as data compression, feature extraction, or model simplification. Minimizing information loss is essential to maintain performance and effectiveness.
- A Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation through Low-Rank Clone
- Accurate and Efficient Low-Rank Model Merging in Core Space
- Effects of Dropout on Performance in Long-range Graph Learning Tasks
- No Loss, No Gain: Gated Refinement and Adaptive Compression for Prompt Optimization
- Point3R: Streaming 3D Reconstruction with Explicit Spatial Pointer Memory
- Re-coding for Uncertainties: Edge-awareness Semantic Concordance for Resilient Event-RGB Segmentation
- Relieving the Over-Aggregating Effect in Graph Transformers
- Restoring Pruned Large Language Models via Lost Component Compensation
- Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations
- WarpGAN: Warping-Guided 3D GAN Inversion with Style-Based Novel View Inpainting