Continuous Autoregressive Models with Noise Augmentation Avoid Error Accumulation (2411.18447v1)

Abstract: Autoregressive models are typically applied to sequences of discrete tokens, but recent research indicates that generating sequences of continuous embeddings in an autoregressive manner is also feasible. However, such Continuous Autoregressive Models (CAMs) can suffer from a decline in generation quality over extended sequences due to error accumulation during inference. We introduce a novel method to address this issue by injecting random noise into the input embeddings during training. This procedure makes the model robust against varying error levels at inference. We further reduce error accumulation through an inference procedure that introduces low-level noise. Experiments on musical audio generation show that CAM substantially outperforms existing autoregressive and non-autoregressive approaches while preserving audio quality over extended sequences. This work paves the way for generating continuous embeddings in a purely autoregressive setting, opening new possibilities for real-time and interactive generative applications.

• The paper introduces a noise-augmented training mechanism that mitigates error accumulation in continuous sequence generation.
• It employs dual-phase noise injection during training and inference to boost model robustness and improve audio generation quality.
• The findings demonstrate strong potential for real-time music and speech applications, outperforming traditional models with key performance metrics.

Evaluation of Continuous Autoregressive Models with Noise Augmentation

The paper presents a compelling paper on Continuous Autoregressive Models (CAMs) enhanced with noise augmentation to tackle error accumulation, a significant challenge in autoregressive generation of continuous data sequences, such as audio embeddings. It methodically explores the implementation of these models to produce high-quality audio outputs over extended sequences while mitigating the common pitfalls associated with generation quality decline during inference.

Technical Approach

The proposed CAM framework introduces a novel noise-augmented training methodology. The authors articulate that while autoregressive models (AMs) have traditionally operated effectively within discrete token spaces, continuous embeddings offer a more compact and efficient representation that can boost inference performance. However, these embeddings are susceptible to error accumulation — a condition where prediction errors propagate and compound over iterations, impacting output quality.

The researchers address this through a dual-component approach involving noise injection both during training and inference. In training, random noise is introduced into the sequence of continuous embeddings, fostering a learning environment where the model can discern and rectify error-prone inputs. This is aimed at enhancing the model's robustness against the variance in error levels encountered during actual application scenarios. A further noise-based intervention in the inference phase helps reinforce the model's resilience against accumulated errors from sequential generation.

Evaluation and Results

The experimental evaluation involves generating musical audio embeddings, a domain where real-time, high-quality generation is critical. The results indicate that CAMs substantially outperform existing architectures, showcasing superior performance when stacked against both autoregressive and non-autoregressive baselines. Specifically, the Frechet Audio Distance (FAD) and $\text{FAD}_{\text{acc}}$ metrics demonstrate lower values, indicating improved fidelity in the generated audio sequences.

In experimental comparisons, CAMs demonstrated a surprisingly effective decrease in FAD when generating longer sequences. This robustness is ascribed to the noise augmentation strategy, which maintains high model performance over extended sequence generation, a notable deviation from traditional models that experience quality degradation as sequence length increases.

The authors' findings suggest the broader implications and potential of adopting CAMs in real-time and interactive audio applications. By paving the way for efficient and error-resilient continuous data modeling, this work could significantly influence the development of systems like real-time music accompaniment and speech-driven conversational interfaces.

Theoretical and Practical Implications

The research illustrates the practicability of CAMs, notably within the audio domain, and establishes a clear roadmap for leveraging the fidelity and efficiency of continuous embeddings. The paper does not solely focus on theoretical advancements; it also brings forth practical interventions that can be directly mapped to AI-driven applications in music and speech processing domains.

Additionally, it aligns with emerging trends in generative models, inviting further investigation into the nuances of noise-conditioned embeddings and their role in enhancing the generative capacity of models across various continuous domains. Future research could expand on these methodologies, potentially adapting them to broader applications like video generation or reinforcement learning environments where error accumulation poses a similar challenge.

In conclusion, the introduction of Continuous Autoregressive Models with noise augmentation represents a pivotal step in overcoming the limitations of autoregressive paradigms within continuous spaces. Through robust experimentation and clearly defined methodologies, this paper contributes significantly to the discourse around the dynamic application and enhancement of generative models in complex data domains.