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Found 13 papers, 5 papers with code
On Noise Injection in Generative Adversarial Networks
Noise injection has been proved to be one of the key technique advances in generating high-fidelity images.
Cones: Concept Neurons in Diffusion Models for Customized Generation
Concatenating multiple clusters of concept neurons can vividly generate all related concepts in a single image.
Cones 2: Customizable Image Synthesis with Multiple Subjects
Synthesizing images with user-specified subjects has received growing attention due to its practical applications.
Low-Rank Subspaces in GANs
Concretely, given an arbitrary image and a region of interest (e. g., eyes of face images), we manage to relate the latent space to the image region with the Jacobian matrix and then use low-rank factorization to discover steerable latent subspaces.
Weakly Supervised High-Fidelity Clothing Model Generation
The development of online economics arouses the demand of generating images of models on product clothes, to display new clothes and promote sales.
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Context Attention Network for Skeleton Extraction
Skeleton extraction is a task focused on providing a simple representation of an object by extracting the skeleton from the given binary or RGB image.
Principled Knowledge Extrapolation with GANs
Human can extrapolate well, generalize daily knowledge into unseen scenarios, raise and answer counterfactual questions.
Rank Diminishing in Deep Neural Networks
By virtue of our numerical tools, we provide the first empirical analysis of the per-layer behavior of network rank in practical settings, i. e., ResNets, deep MLPs, and Transformers on ImageNet.
Neural Dependencies Emerging from Learning Massive Categories
Through investigating the properties of the problem solution, we confirm that neural dependency is guaranteed by a redundant logit covariance matrix, which condition is easily met given massive categories, and that neural dependency is highly sparse, implying that one category correlates to only a few others.
Dimensionality-Varying Diffusion Process
Diffusion models, which learn to reverse a signal destruction process to generate new data, typically require the signal at each step to have the same dimension.
Eliminating Lipschitz Singularities in Diffusion Models
Diffusion models, which employ stochastic differential equations to sample images through integrals, have emerged as a dominant class of generative models.
Regularized Mask Tuning: Uncovering Hidden Knowledge in Pre-trained Vision-Language Models
To bring the useful knowledge back into light, we first identify a set of parameters that are important to a given downstream task, then attach a binary mask to each parameter, and finally optimize these masks on the downstream data with the parameters frozen.
Self-Organizing Pathway Expansion for Non-Exemplar Class-Incremental Learning
Non-exemplar class-incremental learning aims to recognize both the old and new classes without access to old class samples.
