Unlike in the deep learning case, the superior performance of ensemble in the random feature setting cannot be distilled to an individual model. For instance, in Figure 3, the neural tangent kernel (NTK) models’ ensemble achieves 70.54% accuracy on the CIFAR-10 dataset, b...
Data augmentation techniques, e.g., flipping or cropping, which systematically enlarge the training dataset by explicitly generating more training samples, are effective in improving the generalization performance of deep neural networks. In the supervised setting, a common practice for data augmentation ...
Despite its success, we have observed three limitations: The procedure is dataset-dependent, and thus requires the use of expert knowledge. Furthermore, the connection between the synthesized data and the original data is largely ignored in learning, without taking into account the prior distribution...
Learning ULMFiT and Self-Distillation with Calibration for MedicalDialogue SystemShuang AoDoti Health Ltdao.shuang@u.nus.eduXeno AcharyaDoti Health Ltdxeno.acharya@gmail.comAbstractA medical dialogue system is essential forhealthcare service as providing primary clin-ical advice and diagnoses. It has ...
This is the official implementation of Not All Voxels Are Equal: Hardness-Aware Semantic Scene Completion with Self-Distillation (CVPR 2024) [Paper] [Video]. Preparation SemanticKITTI Download The semantic scene completion dataset v1.1 (SemanticKITTI voxel data, 700 MB) from SemanticKITTI website....
One benchmark (VisEvent) is evaluated. Please modify the <DATASET_PATH> and <SAVE_PATH> in./RGBE_workspace/test_rgbe_mgpus.py, then run: Acknowledgments Thanks for theOSTrackandPyTrackinglibrary, which help us to quickly implement our ideas. ...
importance of cluttered scenes in 3D representation learning, and automatically construct a multi-object dataset benefiting from cost-free supervision in ... S Chen,R Garcia,I Laptev,... - IEEE 被引量: 0发表: 2024年 Automatic generation of naturalistic child–adult interaction data Automatic gener...
Results: We validate our proposed framework on the public dataset Cholec80. Our framework is embedded on top of four popular SOTA approaches and consistently improves their performance. Specifically, our best GRU model boosts performance by +3.33% accuracy and +3.95% F1-score over the same ...
Step 4 (optional): if you need to run multi-view feature fusion with OpenSeg (especially for your own dataset), remember to install: pip install tensorflow Data Preparation We provide the pre-processed 3D&2D data and multi-view fused features for the following datasets: ScanNet Matterport3D nu...
DIV2K [42] is a high-quality image dataset, which is widely used in the SISR task. Following previous works, we use DIV2K (1–800) as the training dataset. For testing, we choose Set5 [43], Set14 [44], BSD100 [45], Urban100 [46], and Manga109 [47]. These five datasets are...