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Self-Supervised Scene De-occlusion
We investigate the problem of scene de-occlusion, which aims to recover the underlying occlusion ordering and complete the invisible parts of occluded ...
self-supervised-learning computer-vision de-occlusion research
Talking-Heads Attention
A variation on multi-head attention which includes linear projections across the attention-heads dimension, immediately before and after the softmax ...
multi-head-attention talking-heads-attention attention transformers
Meta Pseudo Labels
We all know about meta-learning and pseudo labeling but what if we combine the two techniques for semi-supervised learning? Can it be any beneficial?
semi-supervised-learning meta-learning machine-learning deep-learning
Towards an ImageNet Moment for Speech-to-Text
An overview of the conditions met by the Speech-to-Text ML subfield to reach the ImageNet moment.
speech-recognition speech-to-text asr russian
TransMoMo: Invariance-Driven Unsupervised Motion Retargeting
A lightweight video motion retargeting approach that is capable of transferring motion of a person in a source video realistically to another video of a ...
motion-generation video retargeting transmomo
How to setup a local AWS SageMaker environment for PyTorch
Learn how to develop an ML app for a PyTorch Model faster by using AWS SageMaker local mode vs. deploying directly to AWS.
pytorch sagemaker web-services web-app
What You Need to Know About Product Management for AI
A product manager for AI does everything a traditional PM does, and much more.
product-management machine-learning business tutorial
Why Batch Norm Causes Exploding Gradients
Our beloved Batch Norm can actually cause exploding gradients, at least at initialization time.
batch-normalization exploding-gradients weights-initialization deep-learning
The Illustrated FixMatch for Semi-Supervised Learning
Learn how to leverage unlabeled data using FixMatch for semi-supervised learning
semi-supervised-learning computer-vision pytorch illustrated-series
What does a CNN see?
First super clean notebook showcasing @TensorFlow 2.0. An example of end-to-end DL with interpretability.
tensorflow interpretability computer-vision tutorial
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