408 research outputs found
Self-critical Sequence Training for Image Captioning
Recently it has been shown that policy-gradient methods for reinforcement
learning can be utilized to train deep end-to-end systems directly on
non-differentiable metrics for the task at hand. In this paper we consider the
problem of optimizing image captioning systems using reinforcement learning,
and show that by carefully optimizing our systems using the test metrics of the
MSCOCO task, significant gains in performance can be realized. Our systems are
built using a new optimization approach that we call self-critical sequence
training (SCST). SCST is a form of the popular REINFORCE algorithm that, rather
than estimating a "baseline" to normalize the rewards and reduce variance,
utilizes the output of its own test-time inference algorithm to normalize the
rewards it experiences. Using this approach, estimating the reward signal (as
actor-critic methods must do) and estimating normalization (as REINFORCE
algorithms typically do) is avoided, while at the same time harmonizing the
model with respect to its test-time inference procedure. Empirically we find
that directly optimizing the CIDEr metric with SCST and greedy decoding at
test-time is highly effective. Our results on the MSCOCO evaluation sever
establish a new state-of-the-art on the task, improving the best result in
terms of CIDEr from 104.9 to 114.7.Comment: CVPR 2017 + additional analysis + fixed baseline results, 16 page
Reinforcement Learning for Generative AI: A Survey
Deep Generative AI has been a long-standing essential topic in the machine
learning community, which can impact a number of application areas like text
generation and computer vision. The major paradigm to train a generative model
is maximum likelihood estimation, which pushes the learner to capture and
approximate the target data distribution by decreasing the divergence between
the model distribution and the target distribution. This formulation
successfully establishes the objective of generative tasks, while it is
incapable of satisfying all the requirements that a user might expect from a
generative model. Reinforcement learning, serving as a competitive option to
inject new training signals by creating new objectives that exploit novel
signals, has demonstrated its power and flexibility to incorporate human
inductive bias from multiple angles, such as adversarial learning,
hand-designed rules and learned reward model to build a performant model.
Thereby, reinforcement learning has become a trending research field and has
stretched the limits of generative AI in both model design and application. It
is reasonable to summarize and conclude advances in recent years with a
comprehensive review. Although there are surveys in different application areas
recently, this survey aims to shed light on a high-level review that spans a
range of application areas. We provide a rigorous taxonomy in this area and
make sufficient coverage on various models and applications. Notably, we also
surveyed the fast-developing large language model area. We conclude this survey
by showing the potential directions that might tackle the limit of current
models and expand the frontiers for generative AI
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