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Found 14 papers, 6 papers with code
H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation
We observe that probability maps by DL semantic segmentation models can be used to generate many possible instance candidates, and accurate instance segmentation can be achieved by selecting from them a set of "optimized" candidates as output instances.
Leveraging unsupervised and weakly-supervised data to improve direct speech-to-speech translation
End-to-end speech-to-speech translation (S2ST) without relying on intermediate text representations is a rapidly emerging frontier of research.
CTIN: Robust Contextual Transformer Network for Inertial Navigation
Recently, data-driven inertial navigation approaches have demonstrated their capability of using well-trained neural networks to obtain accurate position estimates from inertial measurement units (IMU) measurements.
Momentum Centering and Asynchronous Update for Adaptive Gradient Methods
We demonstrate that ACProp has a convergence rate of $O(\frac{1}{\sqrt{T}})$ for the stochastic non-convex case, which matches the oracle rate and outperforms the $O(\frac{logT}{\sqrt{T}})$ rate of RMSProp and Adam.
Posthoc Verification and the Fallibility of the Ground Truth
Metrics used in these evaluations are tied to the availability of well-defined ground truth labels, and these metrics typically do not allow for inexact matches.
Reddit Entity Linking Dataset
We introduce and make publicly available an entity linking dataset from Reddit that contains 17, 316 linked entities, each annotated by three human annotators and then grouped into Gold, Silver, and Bronze to indicate inter-annotator agreement.
AdaBelief Optimizer: Adapting Stepsizes by theBelief in Observed Gradients
We propose AdaBelief optimizer to simultaneously achieve three goals: fast convergence as in adaptive methods, good generalization as in SGD, and training stability.
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
Viewing the exponential moving average (EMA) of the noisy gradient as the prediction of the gradient at the next time step, if the observed gradient greatly deviates from the prediction, we distrust the current observation and take a small step; if the observed gradient is close to the prediction, we trust it and take a large step.
HetSeq: Distributed GPU Training on Heterogeneous Infrastructure
The present work describes HetSeq, a software package adapted from the popular PyTorch package that provides the capability to train large neural network models on heterogeneous infrastructure.
Self-supervised learning for audio-visual speaker diarization
Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems.
Defending Against Adversarial Attacks Using Random Forests
As deep neural networks (DNNs) have become increasingly important and popular, the robustness of DNNs is the key to the safety of both the Internet and the physical world.
Frame-Recurrent Video Inpainting by Robust Optical Flow Inference
Compared with image inpainting, performing this task on video presents new challenges such as how to preserving temporal consistency and spatial details, as well as how to handle arbitrary input video size and length fast and efficiently.
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising.
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
In the first stage, we identify a small portion of images from the noisy training set of which the labels are correct with a high probability.
