9 citations · 21 across the 8 of their papers we have counts for
4 papers · 1 filter
Efficient Scaling of Diffusion Transformers for Text-to-Image Generation
Hao Li, Shamit Lal, Zhiheng Li +9
We empirically study the scaling properties of various Diffusion Transformers (DiTs) for text-to-image generation by performing extensive and rigorous ablations, including training…
On the Scalability of Diffusion-based Text-to-Image Generation
Hao Li, Yang Zou, Ying Wang +7
Scaling up model and data size has been quite successful for the evolution of LLMs. However, the scaling law for the diffusion based text-to-image (T2I) models is not fully explore…
Supervised Momentum Contrastive Learning for Few-Shot Classification
Orchid Majumder, Avinash Ravichandran, Subhransu Maji +3
Few-shot learning aims to transfer information from one task to enable generalization on novel tasks given a few examples. This information is present both in the domain and the cl…
-SNE: Domain Adaptation using Stochastic Neighborhood Embedding
Xiang Xu, Xiong Zhou, Ragav Venkatesan +2
Deep neural networks often require copious amount of labeled-data to train their scads of parameters. Training larger and deeper networks is hard without appropriate regularization…