Multi-modal language-vision models trained on hundreds of millions of
image-text pairs (e.g. CLIP, DALL-E) gained a recent surge, showing remarkable
capability to perform zero- or few-shot learning and transfer even in absence
of per-sample labels on target image data. Despite this trend, to date there
has been no publicly available datasets of sufficient scale for training such
models from scratch. To address this issue, in a community effort we build and
release for public LAION-400M, a dataset with CLIP-filtered 400 million
image-text pairs, their CLIP embeddings and kNN indices that allow efficient
similarity search.

这篇文章介绍了一个公共的数据集 LAION-400M，包含了 CLIP 过滤后的 4 亿对图片 - 文本配对、它们对应的 CLIP 嵌入以及 kNN 索引，能用于训练多模式语言视觉模型，进行零样本或少样本学习和迁移。

LAION-400M：CLIP 过滤的 4 亿张图文对开放数据集

LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reinforcement learning allows solving complex tasks, however, the learning
tends to be task-specific and the sample efficiency remains a challenge. We
present Plan2Explore, a self-supervised reinforcement learning agent that
tackles both these challenges through a new approach to self-supervised
exploration and fast adaptation to new tasks, which need not be known during
exploration. During exploration, unlike prior methods which retrospectively
compute the novelty of observations after the agent has already reached them,
our agent acts efficiently by leveraging planning to seek out expected future
novelty. After exploration, the agent quickly adapts to multiple downstream
tasks in a zero or a few-shot manner. We evaluate on challenging control tasks
from high-dimensional image inputs. Without any training supervision or
task-specific interaction, Plan2Explore outperforms prior self-supervised
exploration methods, and in fact, almost matches the performances oracle which
has access to rewards. Videos and code at
this https URL

Plan2Explore 是一种无须任务特定交互且能够通过自监督探索和快速适应新任务的强化学习机器人，利用计划来探求未来预期的新颖性并在零次或少次学习中快速适应新任务。