Papers › OCD: Learning to Overfit with Conditional Diffusion Models
OCD: Learning to Overfit with Conditional Diffusion Models
Shahar Lutati, Lior Wolf
We present a dynamic model in which the weights are conditioned on an input sample x and are learned to match those that would be obtained by finetuning a base model on x and its label y. This mapping between an input sample and network weights is approximated by a denoising diffusion model. The diffusion model we employ focuses on modifying a single layer of the base model and is conditioned on the input, activations, and output of this layer. Since the diffusion model is stochastic in nature, multiple initializations generate different networks, forming an ensemble, which leads to further improvements. Our experiments demonstrate the wide applicability of the method for image classification, 3D reconstruction, tabular data, speech separation, and natural language processing. Our code is available at https://github.com/ShaharLutatiPersonal/OCD
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Text Classification | Amazon Counterfeit | SetFit + OCD | Accuracy | 0.41 | #1 of 1 | Archive leaderboard | report |
| Few-Shot Text Classification | Average on NLP datasets | SetFit + OCD(5) | Accuracy | 0.648 | #1 of 4 | Archive leaderboard | report |
| Few-Shot Text Classification | Average on NLP datasets | SetFit + OCD | Accuracy | 0.643 | #2 of 4 | Archive leaderboard | report |
| Few-Shot Text Classification | Average on NLP datasets | T-few 3B | Accuracy | 0.633 | #3 of 4 | Archive leaderboard | report |
| Few-Shot Text Classification | Average on NLP datasets | SetFit | Accuracy | 0.622 | #4 of 4 | Archive leaderboard | report |
| Few-Shot Text Classification | SST-5 | SetFit + OCD | Accuracy | 0.478 | #1 of 1 | Archive leaderboard | report |
| Image Classification | Tiny ImageNet Classification | DeiT-B/16-D + OCD(5) | Validation Acc | 92.0% | #2 of 23 | Archive leaderboard | report |
| Image Classification | Tiny ImageNet Classification | DeiT-B/16-D + OCD | Validation Acc | 90.8% | #6 of 23 | Archive leaderboard | report |
| Speech Separation | Libri5Mix | OCD | SI-SDRi | 13.4 | #3 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: OCD
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