Search Results for author:
Found 3 papers, 2 papers with code
Hard Sample Matters a Lot in Zero-Shot Quantization
Nonetheless, we find that the synthetic samples constructed in existing ZSQ methods can be easily fitted by models.
CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection
Open-world object detection (OWOD), as a more general and challenging goal, requires the model trained from data on known objects to detect both known and unknown objects and incrementally learn to identify these unknown objects.
Automatic Subspace Evoking for Efficient Neural Architecture Search
Then, we perform a local search within the evoked subspace to find an effective architecture.
Ranked #2 on
Neural Architecture Search
on NAS-Bench-201, ImageNet-16-120
(Accuracy (Val) metric)
Cannot find the paper you are looking for? You can
Submit a new open access paper.
