Game-invariant Features Through Contrastive and Domain-adversarial Learning

TL;DR

This paper introduces a novel method combining contrastive and domain-adversarial learning to develop game-invariant visual features, enabling better generalization across different games with minimal retraining.

Contribution

It proposes a new approach that effectively learns game-invariant features by integrating contrastive and domain-adversarial training, improving cross-game transferability.

Findings

Features no longer cluster by game after training

Model generalizes well with minimal fine-tuning

Potential for improved cross-game tasks like glitch detection

Abstract

Foundational game-image encoders often overfit to game-specific visual styles, undermining performance on downstream tasks when applied to new games. We present a method that combines contrastive learning and domain-adversarial training to learn game-invariant visual features. By simultaneously encouraging similar content to cluster and discouraging game-specific cues via an adversarial domain classifier, our approach produces embeddings that generalize across diverse games. Experiments on the Bingsu game-image dataset (10,000 screenshots from 10 games) demonstrate that after only a few training epochs, our model's features no longer cluster by game, indicating successful invariance and potential for improved cross-game transfer (e.g., glitch detection) with minimal fine-tuning. This capability paves the way for more generalizable game vision models that require little to no retraining on new games.