Search Results for author:
Found 11 papers, 4 papers with code
Diffusion-Based Conditional Image Editing through Optimized Inference with Guidance
To this end, we derive the representation guidance with a combination of two objectives: maximizing the similarity to the target prompt based on the CLIP score and minimizing the structural distance to the source latent variable.
Diffusion-Based Image-to-Image Translation by Noise Correction via Prompt Interpolation
Our approach revises the original noise prediction network of a pretrained diffusion model by introducing a noise correction term.
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions
Large language models (LLMs) are trained on a vast amount of human-written data, but data providers often remain uncredited.
Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN
Deep learning has made significant advances in computer vision, particularly in image classification tasks.
GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps
From this motivation, we propose a novel saliency-aware mixup method, GuidedMixup, which aims to retain the salient regions in mixup images with low computational overhead.
Variational Distribution Learning for Unsupervised Text-to-Image Generation
We propose a text-to-image generation algorithm based on deep neural networks when text captions for images are unavailable during training.
Information-Theoretic GAN Compression with Variational Energy-based Model
We propose an information-theoretic knowledge distillation approach for the compression of generative adversarial networks, which aims to maximize the mutual information between teacher and student networks via a variational optimization based on an energy-based model.
Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation
We present a novel class incremental learning approach based on deep neural networks, which continually learns new tasks with limited memory for storing examples in the previous tasks.
Class-Incremental Learning for Action Recognition in Videos
We tackle catastrophic forgetting problem in the context of class-incremental learning for video recognition, which has not been explored actively despite the popularity of continual learning.
Operation-Aware Soft Channel Pruning using Differentiable Masks
We propose a simple but effective data-driven channel pruning algorithm, which compresses deep neural networks in a differentiable way by exploiting the characteristics of operations.
Towards Oracle Knowledge Distillation with Neural Architecture Search
We present a novel framework of knowledge distillation that is capable of learning powerful and efficient student models from ensemble teacher networks.
