Say I have a very large dataset of signals that I’m attempting to perform some downstream task on (classification, for instance). My datastream is huge and can’t possibly be held or computed on in memory, so I want to train a model that compresses my data and then performs the downstream task on the compressed data. I would like to compress as much as possible while still maintaining respectable task accuracy. How should I go about this? If inference on compressed data is a well studied topic, could you please point me to some relevant resources? Thanks!
Updated on June 29, 2025 in
Manish Menda
on June 29, 2025
You might also want to look into autoencoder-based quantization or neural compression techniques, especially if you need extremely low-bandwidth representations for deployment.
For instance:
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Vector Quantized-VAEs (VQ-VAE) can produce discrete compressed representations that are both compact and semantically meaningful.
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Works really well when you need compact codes and don’t mind some loss in reconstruction fidelity as long as the classification performance holds.
Additionally, if the signals have a temporal structure (like ECG, sensor data, etc.), you can explore:
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Temporal Convolutional Networks (TCNs) as encoders instead of RNNs (often more efficient).
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Or even transformers with low-rank attention or sparse variants if your sequence lengths are large but you want to retain contextual information.
Finally, for streaming scenarios — I’d also suggest looking into online contrastive learning methods like Online BYOL or using continual learning frameworks to avoid forgetting while updating the encoder.
A good resource to explore:
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“Neural Data Compression” (Google Research blog / arXiv) – gives a solid grounding in modern approaches that mix compression with task-awareness.
Let me know if anyone has benchmarked VIB vs VQ-VAE in streaming setups—would love to hear comparisons!
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Priya Nair
on June 29, 2025
ou want to compress large signal data (that doesn’t fit in memory) into a compact form, while retaining enough information to perform a downstream task (e.g., classification) accurately.
Recommended Approach
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Variational Autoencoder (VAE) + Classifier
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Train a VAE to compress data into latent vectors.
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Add a classification head on the latent.
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Loss = VAE Loss + Classification Loss.
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Variational Information Bottleneck (VIB)
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Directly optimizes for compression vs task utility.
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Minimizes mutual info between input and latent, while maximizing info between latent and label.
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Contrastive Learning (e.g., SimCLR)
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Learn useful compressed embeddings without labels.
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Fine-tune or train a classifier on top later.
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Streaming/Online Training
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Process chunks of data sequentially (not all in memory).
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Use encoders like CNN/RNN for signal windows.
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Tools
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PyTorch Lightning / TensorFlow
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Dataloaders with streaming support
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Latent dimension tuning = compression vs accuracy tradeoff
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