Search Results for author:
Found 24 papers, 17 papers with code
OminiControl: Minimal and Universal Control for Diffusion Transformer
In this paper, we introduce OminiControl, a highly versatile and parameter-efficient framework that integrates image conditions into pre-trained Diffusion Transformer (DiT) models.
Teddy: Efficient Large-Scale Dataset Distillation via Taylor-Approximated Matching
Addressing these concerns, this paper introduces Teddy, a Taylor-approximated dataset distillation framework designed to handle large-scale dataset and enhance efficiency.
LinFusion: 1 GPU, 1 Minute, 16K Image
Modern diffusion models, particularly those utilizing a Transformer-based UNet for denoising, rely heavily on self-attention operations to manage complex spatial relationships, thus achieving impressive generation performance.
Heavy Labels Out! Dataset Distillation with Label Space Lightening
Extensive experiments demonstrate that with only about 0. 003% of the original storage required for a complete set of soft labels, we achieve comparable performance to current state-of-the-art dataset distillation methods on large-scale datasets.
Video-Infinity: Distributed Long Video Generation
Specifically, we propose two coherent mechanisms: Clip parallelism and Dual-scope attention.
Distilled Datamodel with Reverse Gradient Matching
To investigate the impact of changes in training data on a pre-trained model, a common approach is leave-one-out retraining.
MindBridge: A Cross-Subject Brain Decoding Framework
Currently, brain decoding is confined to a per-subject-per-model paradigm, limiting its applicability to the same individual for whom the decoding model is trained.
Mutual-modality Adversarial Attack with Semantic Perturbation
Our approach outperforms state-of-the-art attack methods and can be readily deployed as a plug-and-play solution.
SG-Former: Self-guided Transformer with Evolving Token Reallocation
At the heart of our approach is to utilize a significance map, which is estimated through hybrid-scale self-attention and evolves itself during training, to reallocate tokens based on the significance of each region.
Distribution Shift Inversion for Out-of-Distribution Prediction
Machine learning society has witnessed the emergence of a myriad of Out-of-Distribution (OoD) algorithms, which address the distribution shift between the training and the testing distribution by searching for a unified predictor or invariant feature representation.
Lens-to-lens bokeh effect transformation. NTIRE 2023 challenge report
We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge.
Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer
On the other hand, different from the vanilla version, we adopt a learnable scaling operation on content features before content-style feature interaction, which better preserves the original similarity between a pair of content features while ensuring the stylization quality.
Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate
Existing approaches either apply the holistic style of the style image in a global manner, or migrate local colors and textures of the style image to the content counterparts in a pre-defined way.
Partial Network Cloning
Unlike prior methods that update all or at least part of the parameters in the target network throughout the knowledge transfer process, PNC conducts partial parametric "cloning" from a source network and then injects the cloned module to the target, without modifying its parameters.
Dataset Distillation: A Comprehensive Review
Recent success of deep learning is largely attributed to the sheer amount of data used for training deep neural networks. Despite the unprecedented success, the massive data, unfortunately, significantly increases the burden on storage and transmission and further gives rise to a cumbersome model training process.
Few-Shot Dataset Distillation via Translative Pre-Training
We pre-train the translator on some large datasets like ImageNet so that it requires only a limited number of adaptation steps on the target dataset.
Slimmable Dataset Condensation
In this paper, we explore the problem of slimmable dataset condensation, to extract a smaller synthetic dataset given only previous condensation results.
Dataset Factorization for Condensation
In this paper, we study dataset distillation (DD), from a novel perspective and introduce a \emph{dataset factorization} approach, termed \emph{HaBa}, which is a plug-and-play strategy portable to any existing DD baseline.
Dataset Distillation via Factorization
In this paper, we study \xw{dataset distillation (DD)}, from a novel perspective and introduce a \emph{dataset factorization} approach, termed \emph{HaBa}, which is a plug-and-play strategy portable to any existing DD baseline.
Deep Model Reassembly
Given a collection of heterogeneous models pre-trained from distinct sources and with diverse architectures, the goal of DeRy, as its name implies, is to first dissect each model into distinctive building blocks, and then selectively reassemble the derived blocks to produce customized networks under both the hardware resource and performance constraints.
Learning with Recoverable Forgetting
Life-long learning aims at learning a sequence of tasks without forgetting the previously acquired knowledge.
DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation
Prior approaches, despite the promising results, have relied on either estimating dense attention to compute per-point matching, which is limited to only coarse scales due to the quadratic memory cost, or fixing the number of correspondences to achieve linear complexity, which lacks flexibility.
Paint Transformer: Feed Forward Neural Painting with Stroke Prediction
Neural painting refers to the procedure of producing a series of strokes for a given image and non-photo-realistically recreating it using neural networks.
Ranked #1 on
Object Detection
on A2D
AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer
Finally, the content feature is normalized so that they demonstrate the same local feature statistics as the calculated per-point weighted style feature statistics.
Ranked #5 on
Style Transfer
on StyleBench
