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Found 11 papers, 6 papers with code
Effective Diffusion Transformer Architecture for Image Super-Resolution
In this work, we design an effective diffusion transformer for image super-resolution (DiT-SR) that achieves the visual quality of prior-based methods, but through a training-from-scratch manner.
One Step Diffusion-based Super-Resolution with Time-Aware Distillation
Specifically, we introduce a novel score distillation strategy to align the data distribution between the outputs of the student and teacher models after minor noise perturbation.
U-DiTs: Downsample Tokens in U-Shaped Diffusion Transformers
Diffusion Transformers (DiTs) introduce the transformer architecture to diffusion tasks for latent-space image generation.
LIPT: Latency-aware Image Processing Transformer
Extensive experiments on multiple image processing tasks (e. g., image super-resolution (SR), JPEG artifact reduction, and image denoising) demonstrate the superiority of LIPT on both latency and PSNR.
IPT-V2: Efficient Image Processing Transformer using Hierarchical Attentions
Recent advances have demonstrated the powerful capability of transformer architecture in image restoration.
A Survey on Transformer Compression
Model compression methods reduce the memory and computational cost of Transformer, which is a necessary step to implement large language/vision models on practical devices.
CBQ: Cross-Block Quantization for Large Language Models
Post-training quantization (PTQ) has played a key role in compressing large language models (LLMs) with ultra-low costs.
Data Upcycling Knowledge Distillation for Image Super-Resolution
Knowledge distillation (KD) compresses deep neural networks by transferring task-related knowledge from cumbersome pre-trained teacher models to compact student models.
Ranked #27 on
Image Super-Resolution
on Urban100 - 4x upscaling
Toward Accurate Post-Training Quantization for Image Super Resolution
In this paper, we study post-training quantization(PTQ) for image super resolution using only a few unlabeled calibration images.
AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets
Since the modern deep neural networks are of sophisticated design with complex architecture for the accuracy reason, the diversity on distributions of weights and activations is very high.
Superconductivity and normal-state properties of kagome metal RbV3Sb5 single crystals
We report the discovery of superconductivity and detailed normal-state physical properties of RbV3Sb5 single crystals with V kagome lattice.
Superconductivity Materials Science
