RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Figure 3: Performance of FiLM-Ensemble with varying gain \rho , c.f. Section 3.5 .; Performance of FiLM-Ensemble with varying gain \rho , c.f. Section 3.5 .
Figure 4: Performance of FiLM-Ensemble with varying gain \rho on Cifar-100 using Resnet-34 as backbone with M=2 , c.f. Section B.2 .; Performance of FiLM-Ensemble with varying gain \rho on Cifar-100 using Resnet-34 as backbone with M=2 , c.f. Section B.2 .
Figure 5: Performance of FiLM-Ensemble with varying gain \rho on 6mA-rice-Lv dataset, using CNN-based Deep6mA as backbone with M\in\{2,4,8\} , c.f. Section 3.5 .; Performance of FiLM-Ensemble with varying gain \rho on 6mA-rice-Lv dataset, using CNN-based Deep6mA as backbone with M\in\{2,4,8\} , c.f. Section 3.5 .
Memory and inference complexity comparison (CIFAR-10/100 datasets): Number of additional trainable parameters to have 16 ensemble members for different backbones. The inference time (mult-adds) shown corresponds to the mean GPU time (number of multiply-add operations) required to run a forward pass for a batch of size 1 with 16 ensemble members. The bottom section comprises methods whose forward and backward passes are implemented in parallel over ensemble members. Measurements are done on an NVIDIA GeForce GTX 1080 Ti.
Classification performance on CIFAR-100 with M=4 , using ResNet-18/34 as backbone. Best score for each metric in bold , second-best underlined .
Classification performance for retinal Glaucoma images. Best score for each metric in bold , second-best underlined .
OOD detection for retinal Glaucoma images. Best score in bold , second-best underlined .
CIFAR-10/EfficientNet-B0 performance comparison. M\in\{2,4\} . The best score for each metric is printed bold .