How can I design a pretext task to avoid shortcuts in self supervised learning?

[ScarlettAT]

I’ve been trying to implement a self-supervised learning setup for some unlabeled sensor data, but I’m really struggling to design a pretext task that forces the model to learn useful representations instead of just finding a trivial shortcut. Has anyone else hit this wall where the performance on the downstream task just doesn't improve no matter how you tweak the augmentation?

[KennethJ]

Yeah, I’ve tangled with that exact wall. I kept swapping in new augmentations for the sensor streams—time warps, jitter, masking, you name it—and the downstream metric barely budged. It felt like I was chasing a clever pretext task instead of something that actually helps the real task. After a while the improvements were all fatigue, not gains.

[AmeliaG]

I tried a contrastive setup with short temporal crops and added some noise, but I kept spotting a shortcut the model could latch onto—clock alignment or a boring global cue—so the representation wasn't robust to new data.

[EvelynR]

Maybe the bottleneck isn’t the pretext design at all. The sensor data could drift over time, or the labels on the downstream task depend on something the representation never sees. I ended up dropping features I thought mattered and still hit the same wall; not confident that it's the right root cause.

[EllaYJ]

Could it be the downstream task is the real bottleneck rather than the representation?