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MixSize: Training Convnets With Mixed Image Sizes for Improved Accuracy, Speed and Scale Resiliency
Anonymous....
Published date-01/01/2021
Convolutional neural networks (CNNs) are commonly trained using a fixed spatial image size predetermined for a given model. Although trained on images of a specific size, it is well established …
On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes
Anonymous....
Published date-01/01/2021
3DShapeRepresentation
A neural implicit outputs a number indicating whether the given query point in space is outside, inside, or on a surface. Many prior works have focused on _latent-encoded_ neural implicits, …
The large learning rate phase of deep learning
Anonymous....
Published date-01/01/2021
The choice of initial learning rate can have a profound effect on the performance of deep networks. We present empirical evidence that networks exhibit sharply distinct behaviors at small and …
Contrast to Divide: self-supervised pre-training for learning with noisy labels
Anonymous....
Published date-01/01/2021
ImageClassification, Learningwithnoisylabels
Advances in semi-supervised methods for image classification significantly boosted performance in the learning with noisy labels (LNL) task. Specifically, by discarding the erroneous labels (and keeping the samples), the LNL …
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
Anonymous....
Published date-01/01/2021
ImageGeneration
Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images. In this paper, we study the few-shot image synthesis task for …
Conditional Generative Modeling for De Novo Hierarchical Multi-Label Functional Protein Design
Anonymous....
Published date-01/01/2021
The availability of vast protein sequence information and rich functional annotations thereof has a large potential for protein design applications in biomedicine and synthetic biology. To this date, there exists …