MDL-AE: Investigating the trade-off between compressive fidelity and discriminative utility in self-supervised learning
Bibliographic record
Abstract
Current paradigms in Self-Supervised Learning (SSL) achieve state-of-the-art results through complex, heuristic-driven pretext tasks like contrastive learning or masked image modeling. We propose a departure from these heuristics by reframing SSL through the fundamental Minimum Description Length (MDL) principle. We introduce the MDL-Autoencoder (MDL-AE), learning visual representations by optimizing a Vector Quantized Variational AutoEncoder (VQ-VAE)-based objective for efficient, discrete compression of visual data. Through rigorous experiments on the Canadian Institute for Advanced Research 10 (CIFAR-10), we demonstrate that this compression-driven objective learns a rich vocabulary of local visual concepts. However, we uncover a critical architectural insight: despite learning a visibly superior, higher-fidelity vocabulary, a more powerful tokenizer fails to improve downstream performance. We show that the MDL-AE learns holistic object parts rather than generic, composable primitives. Consequently, a sophisticated Vision Transformer (ViT) head consistently fails to outperform a simple linear probe on the flattened feature map. This architectural mismatch reveals that the nature of the learned representation dictates the optimal downstream architecture. To validate this, we demonstrate that a dedicated self-supervised alignment task, based on Masked Autoencoding of the discrete tokens, resolves this mismatch and dramatically improves performance, bridging the gap between generative fidelity and discriminative utility. Our work provides a compelling case study on co-designing objectives and downstream architectures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".