Modern sensors produce increasingly rich streams of high-resolution data. Due to resource constraints, machine learning systems discard the vast majority of this information via resolution reduction. Compressed-domain learning allows models to operate on compact latent representations, allowing higher effective resolution for the same budget. However, existing compression systems are not ideal for compressed learning. Linear transform coding and end-to-end learned compression systems reduce bitrate, but do not uniformly reduce dimensionality; thus, they do not meaningfully increase efficiency. Generative autoencoders reduce dimensionality, but their adversarial or perceptual objectives lead to significant information loss. To address these limitations, we introduce WaLLoC (Wavelet Learned Lossy Compression), a neural codec architecture that combines linear transform coding with nonlinear dimensionality-reducing autoencoders. WaLLoC sandwiches a shallow, asymmetric autoencoder and entropy bottleneck between an invertible wavelet packet transform. Across several key metrics, WaLLoC outperforms the autoencoders used in state-of-the-art latent diffusion models. WaLLoC does not require perceptual or adversarial losses to represent high-frequency detail, providing compatibility with modalities beyond RGB images and stereo audio. WaLLoC's encoder consists almost entirely of linear operations, making it exceptionally efficient and suitable for mobile computing, remote sensing, and learning directly from compressed data. We demonstrate WaLLoC's capability for compressed-domain learning across several tasks, including image classification, colorization, document understanding, and music source separation. Our code, experiments, and pre-trained audio and image codecs are available at https://ut-sysml.org/walloc

翻译：现代传感器产生日益丰富的高分辨率数据流。由于资源限制，机器学习系统通过降低分辨率丢弃了这些信息中的绝大部分。压缩域学习允许模型在紧凑的潜在表示上运行，从而在相同资源预算下实现更高的有效分辨率。然而，现有的压缩系统并不适用于压缩学习。线性变换编码和端到端学习型压缩系统虽然降低了比特率，但并未均匀降低维度；因此，它们无法实质性地提升效率。生成式自编码器降低了维度，但其对抗性或感知性目标会导致显著的信息损失。为应对这些局限性，我们提出了WaLLoC（小波学习型有损压缩），这是一种将线性变换编码与非线性降维自编码器相结合的神经编解码器架构。WaLLoC在可逆小波包变换之间嵌入了一个浅层、非对称的自编码器和熵瓶颈。在多项关键指标上，WaLLoC的性能超越了当前最先进的潜在扩散模型中使用的自编码器。WaLLoC无需依赖感知或对抗损失即可表示高频细节，从而提供了超越RGB图像和立体声音频的模态兼容性。WaLLoC的编码器几乎完全由线性操作构成，使其具有极高的效率，适用于移动计算、遥感以及直接从压缩数据中学习。我们通过多项任务展示了WaLLoC在压缩域学习中的能力，包括图像分类、着色、文档理解和音乐源分离。我们的代码、实验以及预训练的音频和图像编解码器可在https://ut-sysml.org/walloc获取。