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Found 6 papers, 1 papers with code
Reverse Stable Diffusion: What prompt was used to generate this image?
Our novel learning framework produces excellent results on the aforementioned task, yielding the highest gains when applied on the white-box model.
Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors
We propose an efficient abnormal event detection model based on a lightweight masked auto-encoder (AE) applied at the video frame level.
Ranked #13 on
Anomaly Detection
on CUHK Avenue
Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillation
We propose a very fast frame-level model for anomaly detection in video, which learns to detect anomalies by distilling knowledge from multiple highly accurate object-level teacher models.
Ranked #16 on
Anomaly Detection
on CUHK Avenue
Diffusion Models in Vision: A Survey
Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling.
LeRaC: Learning Rate Curriculum
In this work, we propose a novel curriculum learning approach termed Learning Rate Curriculum (LeRaC), which leverages the use of a different learning rate for each layer of a neural network to create a data-free curriculum during the initial training epochs.
Ranked #3 on
Speech Emotion Recognition
on CREMA-D
Discriminability-enforcing loss to improve representation learning
During the training process, deep neural networks implicitly learn to represent the input data samples through a hierarchy of features, where the size of the hierarchy is determined by the number of layers.
