Summary of Adv-kd: Adversarial Knowledge Distillation For Faster Diffusion Sampling, by Kidist Amde Mekonnen et al.
Adv-KD: Adversarial Knowledge Distillation for Faster Diffusion Samplingby Kidist Amde Mekonnen, Nicola Dall'Asen, Paolo RotaFirst…
Summary of Diffusion Actor-critic: Formulating Constrained Policy Iteration As Diffusion Noise Regression For Offline Reinforcement Learning, by Linjiajie Fang et al.
Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learningby Linjiajie…
Summary of Slight Corruption in Pre-training Data Makes Better Diffusion Models, by Hao Chen et al.
Slight Corruption in Pre-training Data Makes Better Diffusion Modelsby Hao Chen, Yujin Han, Diganta Misra,…
Summary of Improving the Training Of Rectified Flows, by Sangyun Lee et al.
Improving the Training of Rectified Flowsby Sangyun Lee, Zinan Lin, Giulia FantiFirst submitted to arxiv…
Summary of Don’t Drop Your Samples! Coherence-aware Training Benefits Conditional Diffusion, by Nicolas Dufour et al.
Don’t drop your samples! Coherence-aware training benefits Conditional diffusionby Nicolas Dufour, Victor Besnier, Vicky Kalogeiton,…
Summary of Kerascv and Kerasnlp: Vision and Language Power-ups, by Matthew Watson et al.
KerasCV and KerasNLP: Vision and Language Power-Upsby Matthew Watson, Divyashree Shivakumar Sreepathihalli, Francois Chollet, Martin…
Summary of Exploring Diffusion Models’ Corruption Stage in Few-shot Fine-tuning and Mitigating with Bayesian Neural Networks, by Xiaoyu Wu et al.
Exploring Diffusion Models’ Corruption Stage in Few-Shot Fine-tuning and Mitigating with Bayesian Neural Networksby Xiaoyu…
Summary of Transition Path Sampling with Improved Off-policy Training Of Diffusion Path Samplers, by Kiyoung Seong et al.
Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplersby Kiyoung Seong, Seonghyun Park,…
Summary of Learning From Random Demonstrations: Offline Reinforcement Learning with Importance-sampled Diffusion Models, by Zeyu Fang et al.
Learning from Random Demonstrations: Offline Reinforcement Learning with Importance-Sampled Diffusion Modelsby Zeyu Fang, Tian LanFirst…
Summary of Diffusion Policies Creating a Trust Region For Offline Reinforcement Learning, by Tianyu Chen et al.
Diffusion Policies creating a Trust Region for Offline Reinforcement Learningby Tianyu Chen, Zhendong Wang, Mingyuan…