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Found 6 papers, 1 papers with code
Seed Optimization with Frozen Generator for Superior Zero-shot Low-light Enhancement
In this work, we observe that the generators, which are pre-trained on massive natural images, inherently hold the promising potential for superior low-light image enhancement against varying scenarios. Specifically, we embed a pre-trained generator to Retinex model to produce reflectance maps with enhanced detail and vividness, thereby recovering features degraded by low-light conditions. Taking one step further, we introduce a novel optimization strategy, which backpropagates the gradients to the input seeds rather than the parameters of the low-light enhancement model, thus intactly retaining the generative knowledge learned from natural images and achieving faster convergence speed.
QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning
Diffusion models have achieved remarkable success in image generation tasks, yet their practical deployment is restrained by the high memory and time consumption.
Learning Gradient Fields for Scalable and Generalizable Irregular Packing
In this work, we delve deeper into a novel machine learning-based approach that formulates the packing problem as conditional generative modeling.
Leveraging Angular Information Between Feature and Classifier for Long-tailed Learning: A Prediction Reformulation Approach
Deep neural networks still struggle on long-tailed image datasets, and one of the reasons is that the imbalance of training data across categories leads to the imbalance of trained model parameters.
Ranked #21 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Learning Calibrated Uncertainties for Domain Shift: A Distributionally Robust Learning Approach
We propose a framework for learning calibrated uncertainties under domain shifts, where the source (training) distribution differs from the target (test) distribution.
Distributionally Robust Learning for Unsupervised Domain Adaptation
This formulation motivates the use of two neural networks that are jointly trained --- a discriminative network between the source and target domains for density-ratio estimation, in addition to the standard classification network.
