12 papers
DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs
Jongwoo Ko, Tianyi Chen, Sungnyun Kim +4
Despite the success of distillation in large language models (LLMs), most prior work applies identical loss functions to both teacher- and student-generated data. These strategies…
ProCrop: Learning Aesthetic Image Cropping from Professional Compositions
Ke Zhang, Tianyu Ding, Jiachen Jiang +4
Image cropping is crucial for enhancing the visual appeal and narrative impact of photographs, yet existing rule-based and data-driven approaches often lack diversity or require an…
HESSO: Towards Automatic Efficient and User Friendly Any Neural Network Training and Pruning
Tianyi Chen, Xiaoyi Qu, David Aponte +7
Structured pruning is one of the most popular approaches to effectively compress the heavy deep neural networks (DNNs) into compact sub-networks while retaining performance. The ex…
Cat-AIR: Content and Task-Aware All-in-One Image Restoration
Jiachen Jiang, Tianyu Ding, Ke Zhang +5
All-in-one image restoration seeks to recover high-quality images from various types of degradation using a single model, without prior knowledge of the corruption source. However,…
OFER: Occluded Face Expression Reconstruction
Pratheba Selvaraju, Victoria Fernandez Abrevaya, Timo Bolkart +4
Reconstructing 3D face models from a single image is an inherently ill-posed problem, which becomes even more challenging in the presence of occlusions. In addition to fewer availa…
Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and Compression
Xiaoyi Qu, David Aponte, Colby Banbury +5
Structured pruning and quantization are fundamental techniques used to reduce the size of deep neural networks (DNNs) and typically are applied independently. Applying these techni…