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Found 6 papers, 1 papers with code
CMViM: Contrastive Masked Vim Autoencoder for 3D Multi-modal Representation Learning for AD classification
Our proposed framework is built on a masked Vim autoencoder to learn a unified multi-modal representation and long-dependencies contained in 3D medical images.
PMP-Swin: Multi-Scale Patch Message Passing Swin Transformer for Retinal Disease Classification
Specifically, we design a Patch Message Passing (PMP) module based on the Message Passing mechanism to establish global interaction for pathological semantic features and to exploit the subtle differences further between different diseases.
DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification
Existing augmentation methods for speaker verification manipulate the raw signal, which are time-consuming and the augmented samples lack diversity.
Recurrent Off-policy Baselines for Memory-based Continuous Control
When the environment is partially observable (PO), a deep reinforcement learning (RL) agent must learn a suitable temporal representation of the entire history in addition to a strategy to control.
Conditional Level Generation and Game Blending
Prior research has shown variational autoencoders (VAEs) to be useful for generating and blending game levels by learning latent representations of existing level data.
Controllable Level Blending between Games using Variational Autoencoders
We then use this space to generate level segments that combine properties of levels from both games.
