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Found 8 papers, 2 papers with code
DailyMAE: Towards Pretraining Masked Autoencoders in One Day
Recently, masked image modeling (MIM), an important self-supervised learning (SSL) method, has drawn attention for its effectiveness in learning data representation from unlabeled data.
Beyond Accuracy: Statistical Measures and Benchmark for Evaluation of Representation from Self-Supervised Learning
Recently, self-supervised metric learning has raised attention for the potential to learn a generic distance function.
Masked Momentum Contrastive Learning for Zero-shot Semantic Understanding
Self-supervised pretraining (SSP) has emerged as a popular technique in machine learning, enabling the extraction of meaningful feature representations without labelled data.
Variantional autoencoder with decremental information bottleneck for disentanglement
One major challenge of disentanglement learning with variational autoencoders is the trade-off between disentanglement and reconstruction fidelity.
A semantic web approach to uplift decentralized household energy data
In a decentralized household energy system comprised of various devices such as home appliances, electric vehicles, and solar panels, end-users are able to dig deeper into the system's details and further achieve energy sustainability if they are presented with data on the electric energy consumption and production at the granularity of the device.
Object-wise Masked Autoencoders for Fast Pre-training
Furthermore, we investigate the inter-object and intra-object relationship and find that the latter is crucial for self-supervised pre-training.
DEFT: Distilling Entangled Factors by Preventing Information Diffusion
Disentanglement is a highly desirable property of representation owing to its similarity to human understanding and reasoning.
Disentangling Action Sequences: Discovering Correlated Samples
Disentanglement is a highly desirable property of representation due to its similarity with human's understanding and reasoning.
