Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities.
In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them.
A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains.
To accelerate inference and reduce model size while maintaining accuracy, we firstly propose a novel transformer distillation method that is a specially designed knowledge distillation (KD) method for transformer-based models.
#9 best model for Question Answering on SQuAD1.1 dev
We propose AugMix, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions.
Recently, segmentation-based methods are quite popular in scene text detection, as the segmentation results can more accurately describe scene text of various shapes such as curve text.
SOTA for Scene Text Detection on ICDAR 2015
We study the problem of source separation for music using deep learning with four known sources: drums, bass, vocals and other accompaniments.
#2 best model for Music Source Separation on MUSDB18
The majority of the previous methods have formulated the separation problem through the time-frequency representation of the mixed signal, which has several drawbacks, including the decoupling of the phase and magnitude of the signal, the suboptimality of time-frequency representation for speech separation, and the long latency in calculating the spectrograms.
Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks.
Model efficiency has become increasingly important in computer vision.
SOTA for Object Detection on COCO 2017 (mAP metric )