Questions tagged [self-supervised-learning]
Self-supervised learning (SSL) is a method of machine learning. It learns from unlabeled sample data.
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Depth estimation used in SSL from ego-motion
I have read a recent paper called Kick Back & Relax: Learning to Reconstruct the
World by Watching SlowTV which tooks inspiration from a famous older one Unsupervised Learning of Depth and Ego-...
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Why can't SSL handle tabular data?
Performance Analysis of Self-Supervised Strategies for Standard Genetic Programming
1 INTRODUCTION
Self-supervised learning (SSL) methods have been widely used to train deep learning models for ...
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Examples of unsupervised neural networks that are not self-supervised
I'm trying to understand if such things exist. For the NN to learn it seems that we need a measure of error and thus we need labels. In the case of auto-encodoers, which are considered unsupervised, ...
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Is there a better way to self-train on tabular data?
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I'm training a classifier on some fraud data. Only a chunk of data is labeled (~2000) so I'm trying a self-training approach, what I'm doing for now is:
Iteratively training a model then ...
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Self-supervised Target Definition in the Original Neural Language Model by Bengio et al (2003)
I understand how later neural language models (such as those used in the Word2Vec papers) framed the language modelling problem in a self-supervised way by learning to predict the next word (or any ...
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Approaches for semi-supervised fine-tuning after self-supervised pre-training
My understanding is that self-supervised learning approaches approximately work like the following (I have Wav2Vec 2 in my mind here, used in speech recognition, but NLP transformer models are similar)...
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Chicken and egg problem in machine learning [closed]
Recently, I went through an ICLR paper SELF-LABELLING VIA SIMULTANEOUS CLUSTERING
AND REPRESENTATION LEARNING. In the paper, authors discussed simultaneously labeling the images and training a network ...
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Can self-supervised pretraining work with only labeled data?
I am working on an image classification problem with only a few samples (10 images). As part of the challenge, we aren't allowed to use any external data or pretrained models. I was wondering whether ...
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What are the SOTA Visual Representation Learning architectures for binary images?
I want to learn the visual representation of binary images such as:
This may later be used for the shape classification problem. I have read 2 state-of-the-art visual representation learning ...
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How is inpainting for self-supervised pre-training of convolutional neural networks usually done?
I read a nice blog post on self-supervised learning and computer vision, which suggests in-painting (amongst other ideas) as a possible self-supervised task for a neural network to "adapt" ...
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Why limit the volume of the autoencoder in self supervised learning?
In this article: self-supervised learning: The dark matter of intelligence the authors tried to unify the self-supervised learning for tasks with discrete outcomes and continuous outcomes. They ...
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