In a wide range of multimodal tasks, contrastive learning has become a
particularly appealing approach since it can successfully learn representations
from abundant unlabeled data with only pairing information (e.g., image-caption
or video-audio pairs). Underpinning these approaches is the assumption of
multi-view redundancy - that shared information between modalities is necessary
and sufficient for downstream tasks. However, in many real-world settings,
task-relevant information is also contained in modality-unique regions:
information that is only present in one modality but still relevant to the
task. How can we learn self-supervised multimodal representations to capture
both shared and unique information relevant to downstream tasks? This paper
proposes FactorCL, a new multimodal representation learning method to go beyond
multi-view redundancy. FactorCL is built from three new contributions: (1)
factorizing task-relevant information into shared and unique representations,
(2) capturing task-relevant information via maximizing MI lower bounds and
removing task-irrelevant information via minimizing MI upper bounds, and (3)
multimodal data augmentations to approximate task relevance without labels. On
large-scale real-world datasets, FactorCL captures both shared and unique
information and achieves state-of-the-art results on six benchmarks.

本文提出了 FactorCL，一种新的多模态表示学习方法，旨在捕获有助于下游任务的共享信息和独特信息，该方法通过将任务相关信息分解为共享和独特表示，并通过最大化 MI 下限来捕获任务相关信息以及通过最小化 MI 上限来消除任务不相关的信息，并使用多模态数据增强来近似任务相关性。实验证明 FactorCL 在六项基准测试上取得了最先进的结果。

因式对比学习：超越多视角冗余

Factorized Contrastive Learning: Going Beyond Multi-view Redundancy

Soft prompt tuning achieves superior performances across a wide range of
few-shot tasks. However, the performances of prompt tuning can be highly
sensitive to the initialization of the prompts. We also empirically observe
that conventional prompt tuning methods cannot encode and learn sufficient
task-relevant information from prompt tokens. In this work, we develop an
information-theoretic framework that formulates soft prompt tuning as
maximizing mutual information between prompts and other model parameters (or
encoded representations). This novel view helps us to develop a more efficient,
accurate and robust soft prompt tuning method InfoPrompt. With this framework,
we develop two novel mutual information based loss functions, to (i) discover
proper prompt initialization for the downstream tasks and learn sufficient
task-relevant information from prompt tokens and (ii) encourage the output
representation from the pretrained language model to be more aware of the
task-relevant information captured in the learnt prompt. Extensive experiments
validate that InfoPrompt can significantly accelerate the convergence of the
prompt tuning and outperform traditional prompt tuning methods. Finally, we
provide a formal theoretical result for showing to show that gradient descent
type algorithm can be used to train our mutual information loss.

该研究提出了一种信息理论框架，用于将软提示调整作为最大化提示和其他模型参数（或编码表示）之间的互信息的方法，从而发展了一种更为高效、准确和稳健的软提示调整方法 InfoPrompt。

信息论软提示调整用于自然语言理解

InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural  Language Understanding

The goal of meta-reinforcement learning (meta-RL) is to build agents that can
quickly learn new tasks by leveraging prior experience on related tasks.
Learning a new task often requires both exploring to gather task-relevant
information and exploiting this information to solve the task. In principle,
optimal exploration and exploitation can be learned end-to-end by simply
maximizing task performance. However, such meta-RL approaches struggle with
local optima due to a chicken-and-egg problem: learning to explore requires
good exploitation to gauge the exploration's utility, but learning to exploit
requires information gathered via exploration. Optimizing separate objectives
for exploration and exploitation can avoid this problem, but prior meta-RL
exploration objectives yield suboptimal policies that gather information
irrelevant to the task. We alleviate both concerns by constructing an
exploitation objective that automatically identifies task-relevant information
and an exploration objective to recover only this information. This avoids
local optima in end-to-end training, without sacrificing optimal exploration.
Empirically, DREAM substantially outperforms existing approaches on complex
meta-RL problems, such as sparse-reward 3D visual navigation. Videos of DREAM:
this https URL

本文提出一种基于策略梯度优化的元强化学习方法，该方法不但可以在学习新任务时快速利用之前相关任务的经验，还能自动识别任务相关信息来避免局部最优解。实验结果表明，该方法可以有效解决如稀疏奖励 3D 视觉导航等复杂问题。

元强化学习的探索与利用解耦，无需牺牲

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning  without Sacrifices

As a subset of unsupervised representation learning, self-supervised
representation learning adopts self-defined signals as supervision and uses the
learned representation for downstream tasks, such as object detection and image
captioning. Many proposed approaches for self-supervised learning follow
naturally a multi-view perspective, where the input (e.g., original images) and
the self-supervised signals (e.g., augmented images) can be seen as two
redundant views of the data. Building from this multi-view perspective, this
paper provides an information-theoretical framework to better understand the
properties that encourage successful self-supervised learning. Specifically, we
demonstrate that self-supervised learned representations can extract
task-relevant information and discard task-irrelevant information. Our
theoretical framework paves the way to a larger space of self-supervised
learning objective design. In particular, we propose a composite objective that
bridges the gap between prior contrastive and predictive learning objectives,
and introduce an additional objective term to discard task-irrelevant
information. To verify our analysis, we conduct controlled experiments to
evaluate the impact of the composite objectives. We also explore our
framework's empirical generalization beyond the multi-view perspective, where
the cross-view redundancy may not be clearly observed.

本文提供了一种信息理论框架，从多个视角探究自监督学习的特性，并介绍了可以丢弃任务无关信息的任务相关自适应目标函数，为自监督学习的目标设计提供了更加广阔的空间。