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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 …
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search Space
Anonymous....
Published date-01/01/2021
ImageClassification, NeuralArchitectureSearch
Random-sampling Neural Architecture Search (RandomNAS) has recently become a prevailing NAS approach because of its search efficiency and simplicity. There are two main steps in RandomNAS: the training step that …
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 …
Ruminating Word Representations with Random Noise Masking
Anonymous....
Published date-01/01/2021
TextClassification, WordEmbeddings
We introduce a training method for better word representation and performance, which we call \textbf{GraVeR} (\textbf{Gra}dual \textbf{Ve}ctor \textbf{R}umination). The method is to gradually and iteratively add random noises and bias …
Hierarchical Meta Reinforcement Learning for Multi-Task Environments
Anonymous....
Published date-01/01/2021
HierarchicalReinforcementLearning, MetaReinforcementLearning
Deep reinforcement learning algorithms aim to achieve human-level intelligence by solving practical decisions-making problems, which are often composed of multiple sub-tasks. Complex and subtle relationships between sub-tasks make traditional methods …
Uniform Manifold Approximation with Two-phase Optimization
Anonymous....
Published date-01/01/2021
DimensionalityReduction
We present a dimensionality reduction algorithm called Uniform Manifold Approximation with Two-phase Optimization (UMATO), that aims to preserve both the global and local structures of high-dimensional data. Most existing dimensionality …