On Duality Gap as a Measure for Monitoring GAN Training
- URL: http://arxiv.org/abs/2012.06723v1
- Date: Sat, 12 Dec 2020 04:32:52 GMT
- Title: On Duality Gap as a Measure for Monitoring GAN Training
- Authors: Sahil Sidheekh, Aroof Aimen, Vineet Madan, Narayanan C. Krishnan
- Abstract summary: Generative adversarial network (GAN) is among the most popular deep learning models for learning complex data distributions.
This paper presents a theoretical understanding of this limitation and proposes a more dependable estimation process for the duality gap.
- Score: 2.733700237741334
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generative adversarial network (GAN) is among the most popular deep learning
models for learning complex data distributions. However, training a GAN is
known to be a challenging task. This is often attributed to the lack of
correlation between the training progress and the trajectory of the generator
and discriminator losses and the need for the GAN's subjective evaluation. A
recently proposed measure inspired by game theory - the duality gap, aims to
bridge this gap. However, as we demonstrate, the duality gap's capability
remains constrained due to limitations posed by its estimation process. This
paper presents a theoretical understanding of this limitation and proposes a
more dependable estimation process for the duality gap. At the crux of our
approach is the idea that local perturbations can help agents in a zero-sum
game escape non-Nash saddle points efficiently. Through exhaustive
experimentation across GAN models and datasets, we establish the efficacy of
our approach in capturing the GAN training progress with minimal increase to
the computational complexity. Further, we show that our estimate, with its
ability to identify model convergence/divergence, is a potential performance
measure that can be used to tune the hyperparameters of a GAN.
Related papers
- Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning [24.63284452991301]
We propose a doubly robust causal effect estimator under networked interference.
Specifically, we generalize the targeted learning technique into the networked interference setting.
We devise an end-to-end causal effect estimator by transforming the identified theoretical condition into a targeted loss.
arXiv Detail & Related papers (2024-05-06T10:49:51Z) - Neural Network Approximation for Pessimistic Offline Reinforcement
Learning [17.756108291816908]
We present a non-asymptotic estimation error of pessimistic offline RL using general neural network approximation.
Our result shows that the estimation error consists of two parts: the first converges to zero at a desired rate on the sample size with partially controllable concentrability, and the second becomes negligible if the residual constraint is tight.
arXiv Detail & Related papers (2023-12-19T05:17:27Z) - Time-series Generation by Contrastive Imitation [87.51882102248395]
We study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy.
At inference, the learned policy serves as the generator for iterative sampling, and the learned energy serves as a trajectory-level measure for evaluating sample quality.
arXiv Detail & Related papers (2023-11-02T16:45:25Z) - Distributional Shift-Aware Off-Policy Interval Estimation: A Unified
Error Quantification Framework [8.572441599469597]
We study high-confidence off-policy evaluation in the context of infinite-horizon Markov decision processes.
The objective is to establish a confidence interval (CI) for the target policy value using only offline data pre-collected from unknown behavior policies.
We show that our algorithm is sample-efficient, error-robust, and provably convergent even in non-linear function approximation settings.
arXiv Detail & Related papers (2023-09-23T06:35:44Z) - Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
We propose a framework for efficient and effective counterfactual inference implemented with neural networks.
The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data.
Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
arXiv Detail & Related papers (2023-06-09T08:30:51Z) - Counterfactual Maximum Likelihood Estimation for Training Deep Networks [83.44219640437657]
Deep learning models are prone to learning spurious correlations that should not be learned as predictive clues.
We propose a causality-based training framework to reduce the spurious correlations caused by observable confounders.
We conduct experiments on two real-world tasks: Natural Language Inference (NLI) and Image Captioning.
arXiv Detail & Related papers (2021-06-07T17:47:16Z) - Characterizing GAN Convergence Through Proximal Duality Gap [3.0724051098062097]
We show theoretically that the proximal duality gap is capable of monitoring the convergence of GANs to a wider spectrum of equilibria.
We also establish the relationship between the proximal duality gap and the divergence between the real and generated data distributions for different GAN formulations.
arXiv Detail & Related papers (2021-05-11T06:27:27Z) - Accurate and Robust Feature Importance Estimation under Distribution
Shifts [49.58991359544005]
PRoFILE is a novel feature importance estimation method.
We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
arXiv Detail & Related papers (2020-09-30T05:29:01Z) - Generalization Properties of Optimal Transport GANs with Latent
Distribution Learning [52.25145141639159]
We study how the interplay between the latent distribution and the complexity of the pushforward map affects performance.
Motivated by our analysis, we advocate learning the latent distribution as well as the pushforward map within the GAN paradigm.
arXiv Detail & Related papers (2020-07-29T07:31:33Z) - An Information Bottleneck Approach for Controlling Conciseness in
Rationale Extraction [84.49035467829819]
We show that it is possible to better manage this trade-off by optimizing a bound on the Information Bottleneck (IB) objective.
Our fully unsupervised approach jointly learns an explainer that predicts sparse binary masks over sentences, and an end-task predictor that considers only the extracted rationale.
arXiv Detail & Related papers (2020-05-01T23:26:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.