¿Te imaginas un clasificador de imágenes que resuelva casi cualquier problema y no necesite ningún entrenamiento? ¿Has presentado? ¿Resulta que este debería ser un clasificador universal? ¡Así es! Esta es una nueva red neuronal CLIP de OpenAI. Analizando CLIP del encabezado: Desmontaje y montaje de redes neuronales utilizando Star Wars como ejemplo.
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# image_encoder - ResNet or Vision Transformer
# text_encoder - CBOW or Text Transformer
# I[n, h, w, c] - minibatch of aligned images
# T[n, l] - minibatch of aligned texts
# W_i[d_i, d_e] - learned proj of image to embed
# W_t[d_t, d_e] - learned proj of text to embed
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I_f = image_encoder(I) #[n, d_i]
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logits = np.dot(I_e, T_e.T) * np.exp(t)
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labels = np.arange(n)
loss_i = cross_entropy_loss(logits, labels, axis=0)
loss_t = cross_entropy_loss(logits, labels, axis=1)
loss = (loss_i + loss_t)/2
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