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Found 8 papers, 6 papers with code
Understanding Masked Autoencoders via Hierarchical Latent Variable Models
In this work, we formally characterize and justify existing empirical insights and provide theoretical guarantees of MAE.
Face-to-Face Contrastive Learning for Social Intelligence Question-Answering
Creating artificial social intelligence - algorithms that can understand the nuances of multi-person interactions - is an exciting and emerging challenge in processing facial expressions and gestures from multimodal videos.
Conditional Contrastive Learning with Kernel
Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables.
Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning
In this paper, we propose a Conditional Contrastive Learning (CCL) approach to improve the fairness of contrastive SSL methods.
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?
These so-called internal strategies solely rely on the input data (without labels) and the output (outlier scores) of the candidate models.
Self-supervised Representation Learning with Relative Predictive Coding
This paper introduces Relative Predictive Coding (RPC), a new contrastive representation learning objective that maintains a good balance among training stability, minibatch size sensitivity, and downstream task performance.
Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis
The human language can be expressed through multiple sources of information known as modalities, including tones of voice, facial gestures, and spoken language.
Complex Transformer: A Framework for Modeling Complex-Valued Sequence
While deep learning has received a surge of interest in a variety of fields in recent years, major deep learning models barely use complex numbers.
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