一文带你了解OpenAI Sora
Published:
This post explains the basics of OpenAI Sora, a new text-to-video technology developed by OpenAI. Check out the post for more details.
Blog posts
2024
2023
Inpaint Anything: Segment Anything Meets Image Inpainting
Published:
We introduce Inpaint Anything (IA), a mask-free image inpainting system based on the Segment-Anything Model (SAM). IA offers a “clicking and filling” paradigm, combining different models to create a powerful, user-friendly pipeline for inpainting tasks. It supports three features: Remove Anything, Fill Anything, and Replace Anything, allowing users to remove objects, fill holes with generative content, or replace backgrounds with new scenes. Check out the post for more details!
A Closer Look at ChatGPT
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This post explains the details of the ChatGPT model and how it works. Check out the post for more details.
2022
Gato: A Generalist Agent (An RL Foundation Model)
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Paper reading and summary of Gato. Chech out the post for more details.
DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting
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Paper reading and summary of DenseCLIP. Chech out the post for more details.
CoCoOp: Conditional Prompt Learning for Vision-Language Models
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Paper reading and summary of CoCoOp. Chech out the post for more details.
CoOp: Learning to Prompt for Vision-Language Models
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Paper reading and summary of CoOp. Chech out the post for more details.
CLIP: Learning Transferable Visual Models From Natural Language Supervision
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Paper reading and summary of CLIP. Chech out the post for more details.
2021
从MuZero到EfficientZero：Make MBRL Great Again
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I recap the recent progress of MuZero and EfficientZero in the field of Model-Based Reinforcement Learning (MBRL). Chech out the post for more details.
Prioritized Experience Replay
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Paper reading and summary of Prioritized Experience Replay (PER). Chech out the post for more details.
【Offline RL】再读BCQ
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Paper reading and summary of BCQ. Chech out the post for more details.
RL Transformer之Decision Transformer
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Paper reading and summary of Decision Transformer. Chech out the post for more details.
【IS视角下的PPO】从Importance Sampling（IS）到PPO
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I recap the Importance Sampling (IS) and Proximal Policy Optimization (PPO) algorithms in the context of RL. Chech out the post for more details.
光速推导REINFORCE
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Paper reading and summary of REINFORCE. Chech out the post for more details.
RL Transformer之Trajectory Transformers
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Paper reading and summary of Trajectory Transformers. Chech out the post for more details.