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tactile_vae

ViT-based tactile variational autoencoder.

Architecture

  • Encoder: PatchEmbed + Transformer blocks + fixed sin-cos positional embedding
  • Latent: mu/logvar + reparameterization
  • Decoder: latent-conditioned transformer patch decoder + unpatchify

Usage

import torch
from tactile_vae.model import TactileVAE, VAELoss

model = TactileVAE()
out = model(torch.randn(2, 3, 128, 128))
loss_fn = VAELoss(beta=1.0)
losses = loss_fn(out["x_hat"], torch.randn(2, 3, 128, 128), out["mu"], out["logvar"])
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