Experiments
In the following, we first empirically examine the ability of FiLM-Ensemble to learn independent ensemble members, and compare it to deep ensembles. Then we analyze the predictive accuracy and uncertainty calibration of our method, as well as its computational cost and memory consumption. We compare its performance against several baseline methods and state-of-the-art alternatives on a variety of different datasets, and using different backbone architectures.
All experimental results are averaged over three runs with different random seeds.

We evaluate our proposed method on a diverse set of classification tasks, including popular image classification benchmarks, image-based medical diagnosis, and genome sequence analysis. CIFAR-10 and CIFAR-100 ( Krizhevsky , 2009 ) are widely used testbeds for image classification, and deep learning in general. They consist of clean images of objects from 10, respectively 100, different semantic classes. Each of the two datasets contains 60,000 images, of which 10,000 are reserved for testing. The semantic classes are uniformly distributed in the datasets and stratified across the train/test spit. Retina Glaucoma Detection ( Diaz-Pinto et al. , 2019 ) is a real-world clinical dataset that includes microscopic retina images from 956 patients with the neuropathic disease Glaucoma, and from 1401 subjects with normal (healthy) retinas. Each input sample is a single RGB image; we resize all images to 128\times 128 . The image augmentation applies by combination of crop, horizontal flip, and color jitter. REFUGE 2020 ( Orlando et al. , 2020 ) was a challenge at the MICCAI-2020 conference, aimed at retinal Glaucoma diagnosis. The dataset consists of 800 microscopic retina images with size 1411\times 1411 pixels, collected from different clinics. We use this dataset in conjunction with the models trained on the previous dataset ( Diaz-Pinto et al. , 2019 ) , to evaluate the ability to detect out-of-distribution samples with the help of the predicted uncertainties (Table 4 ). 6mA Identification ( Li et al. , 2021 ) , is a 1-dimensional sequential genome dataset.
It consists of DNA sequences of rice plants, along with binary labels that indicate whether the sequence is a N6-methyladenine (6mA) site. 6mA is an important DNA modification associated with several biological processes, such as regulating gene transcription, DNA replication and DNA repair ( Campbell and Kleckner , 1990 ; Cheng et al. , 2016 ; Pukkila et al. , 1983 ) . Each sequence consists of 41 nucleotides. As there are 4 different types of nucleotides, each one-hot encoded sequence is represented by a 41\times 4 matrix. In total there are 269,500 training samples and 38,500 test samples.

We compare FiLM-Ensemble against (i) a single model without any ensembling, as the elementary baseline, (ii) a naïve deep ensemble ( Lakshminarayanan et al. , 2017b ) , and (iii) MC-Dropout ( Gal and Ghahramani , 2016 ) .
Furthermore, we compare with other state-of-the-art methods, including: (iv) Masksemble ( Durasov et al. , 2021 ) which can be seen as an extension of MC-Dropout, (v) MIMO ("multi-input multi-output") ( Havasi et al. , 2021 ) which defines a multi-head architecture where each head acts as one ensemble member, and (vi) BatchEnsemble ( Wen et al. , 2020 ) which creates an efficient ensemble by expanding the layer weights using low rank matrices.
Arguably, this layer-wise modification of an underlying, common representation is the approach that comes closest to our work.