1 citations · 1 across the 5 of their papers we have counts for
5 papers
RewardFlow: Generate Images by Optimizing What You Reward
Onkar Susladkar, Dong-Hwan Jang, Tushar Prakash +7
We introduce RewardFlow, an inversion-free framework that steers pretrained diffusion and flow-matching models at inference time through multi-reward Langevin dynamics. RewardFlow…
PyraTok: Language-Aligned Pyramidal Tokenizer for Video Understanding and Generation
Onkar Susladkar, Tushar Prakash, Adheesh Juvekar +4
Discrete video VAEs underpin modern text-to-video generation and video understanding systems, yet existing tokenizers typically learn visual codebooks at a single scale with limite…
Merge and Bound: Direct Manipulations on Weights for Class Incremental Learning
Taehoon Kim, Donghwan Jang, Bohyung Han
We present a novel training approach, named Merge-and-Bound (M&B) for Class Incremental Learning (CIL), which directly manipulates model weights in the parameter space for optimiza…
Model Stock: All we need is just a few fine-tuned models
Dong-Hwan Jang, Sangdoo Yun, Dongyoon Han
This paper introduces an efficient fine-tuning method for large pre-trained models, offering strong in-distribution (ID) and out-of-distribution (OOD) performance. Breaking away fr…
Pooling Revisited: Your Receptive Field is Suboptimal
Dong-Hwan Jang, Sanghyeok Chu, Joonhyuk Kim +1
The size and shape of the receptive field determine how the network aggregates local information and affect the overall performance of a model considerably. Many components in a ne…