Ace.at_blacked.1.var Official

While the exact internal naming convention ( AT_Blacked.1.var ) may be specific to a particular implementation or dataset (such as those involving "blacked-out" or erased NSFW/NSFW-adjacent concepts), it functions as a that guides the diffusion model to unlearn a targeted visual concept.

: The framework uses these features to improve the model's resistance to prompt-based attacks that try to bypass concept erasure. ace.AT_Blacked.1.var

: ACE introduces learnable gating mechanisms in the model's cross-attention layers, which are fine-tuned per concept using these deep feature representations. While the exact internal naming convention ( AT_Blacked

: These features are typically extracted from deep layers of a neural network (such as the last fully connected layer of a pretrained VGGNet or similar architecture) to capture complex abstract information. : These features are typically extracted from deep

Deep Feature Consistent Variational Autoencoder - IEEE Xplore

In the context of the ACE framework, this "deep feature" likely represents a high-dimensional vector in the model's . Key aspects of these features include:

: The variable represents a specific semantic direction that the ACE method attempts to remove or "erase" to prevent the model from generating undesirable images.